rocksdb/tools/trace_analyzer_tool.cc

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RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
#ifndef ROCKSDB_LITE
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS
#endif
#ifdef GFLAGS
#ifdef NUMA
#include <numa.h>
#include <numaif.h>
#endif
#ifndef OS_WIN
#include <unistd.h>
#endif
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <memory>
#include <sstream>
#include <stdexcept>
#include "db/db_impl.h"
#include "db/memtable.h"
#include "db/write_batch_internal.h"
#include "options/cf_options.h"
#include "rocksdb/db.h"
#include "rocksdb/env.h"
#include "rocksdb/iterator.h"
#include "rocksdb/slice.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/status.h"
#include "rocksdb/table_properties.h"
#include "rocksdb/utilities/ldb_cmd.h"
#include "rocksdb/write_batch.h"
#include "table/meta_blocks.h"
#include "table/plain_table_factory.h"
#include "table/table_reader.h"
#include "tools/trace_analyzer_tool.h"
#include "util/coding.h"
#include "util/compression.h"
#include "util/file_reader_writer.h"
#include "util/gflags_compat.h"
#include "util/random.h"
#include "util/string_util.h"
#include "util/trace_replay.h"
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
using GFLAGS_NAMESPACE::RegisterFlagValidator;
using GFLAGS_NAMESPACE::SetUsageMessage;
DEFINE_string(trace_path, "", "The trace file path.");
DEFINE_string(output_dir, "", "The directory to store the output files.");
DEFINE_string(output_prefix, "trace",
"The prefix used for all the output files.");
DEFINE_bool(output_key_stats, false,
"Output the key access count statistics to file\n"
"for accessed keys:\n"
"file name: <prefix>-<query_type>-<cf_id>-accessed_key_stats.txt\n"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"Format:[cf_id value_size access_keyid access_count]\n"
"for the whole key space keys:\n"
"File name: <prefix>-<query_type>-<cf_id>-whole_key_stats.txt\n"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
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"Format:[whole_key_space_keyid access_count]");
DEFINE_bool(output_access_count_stats, false,
"Output the access count distribution statistics to file.\n"
"File name: <prefix>-<query_type>-<cf_id>-accessed_"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"key_count_distribution.txt \n"
"Format:[access_count number_of_access_count]");
DEFINE_bool(output_time_series, false,
"Output the access time in second of each key, "
"such that we can have the time series data of the queries \n"
"File name: <prefix>-<query_type>-<cf_id>-time_series.txt\n"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"Format:[type_id time_in_sec access_keyid].");
DEFINE_int32(output_prefix_cut, 0,
"The number of bytes as prefix to cut the keys.\n"
"If it is enabled, it will generate the following:\n"
"For accessed keys:\n"
"File name: <prefix>-<query_type>-<cf_id>-"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"accessed_key_prefix_cut.txt \n"
"Format:[acessed_keyid access_count_of_prefix "
"number_of_keys_in_prefix average_key_access "
"prefix_succ_ratio prefix]\n"
"For whole key space keys:\n"
"File name: <prefix>-<query_type>-<cf_id>"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"-whole_key_prefix_cut.txt\n"
"Format:[start_keyid_in_whole_keyspace prefix]\n"
"if 'output_qps_stats' and 'top_k' are enabled, it will output:\n"
"File name: <prefix>-<query_type>-<cf_id>"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
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"-accessed_top_k_qps_prefix_cut.txt\n"
"Format:[the_top_ith_qps_time QPS], [prefix qps_of_this_second].");
DEFINE_bool(convert_to_human_readable_trace, false,
"Convert the binary trace file to a human readable txt file "
"for further processing. "
"This file will be extremely large "
"(similar size as the original binary trace file). "
"You can specify 'no_key' to reduce the size, if key is not "
"needed in the next step.\n"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"File name: <prefix>_human_readable_trace.txt\n"
"Format:[type_id cf_id value_size time_in_micorsec <key>].");
DEFINE_bool(output_qps_stats, false,
"Output the query per second(qps) statistics \n"
"For the overall qps, it will contain all qps of each query type. "
"The time is started from the first trace record\n"
"File name: <prefix>_qps_stats.txt\n"
"Format: [qps_type_1 qps_type_2 ...... overall_qps]\n"
"For each cf and query, it will have its own qps output.\n"
"File name: <prefix>-<query_type>-<cf_id>_qps_stats.txt \n"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"Format:[query_count_in_this_second].");
DEFINE_bool(no_print, false, "Do not print out any result");
DEFINE_string(
print_correlation, "",
"intput format: [correlation pairs][.,.]\n"
"Output the query correlations between the pairs of query types "
"listed in the parameter, input should select the operations from:\n"
"get, put, delete, single_delete, rangle_delete, merge. No space "
"between the pairs separated by commar. Example: =[get,get]... "
"It will print out the number of pairs of 'A after B' and "
"the average time interval between the two query.");
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
DEFINE_string(key_space_dir, "",
"<the directory stores full key space files> \n"
"The key space files should be: <column family id>.txt");
DEFINE_bool(analyze_get, false, "Analyze the Get query.");
DEFINE_bool(analyze_put, false, "Analyze the Put query.");
DEFINE_bool(analyze_delete, false, "Analyze the Delete query.");
DEFINE_bool(analyze_single_delete, false, "Analyze the SingleDelete query.");
DEFINE_bool(analyze_range_delete, false, "Analyze the DeleteRange query.");
DEFINE_bool(analyze_merge, false, "Analyze the Merge query.");
DEFINE_bool(analyze_iterator, false,
" Analyze the iterate query like seek() and seekForPrev().");
DEFINE_bool(no_key, false,
" Does not output the key to the result files to make smaller.");
DEFINE_bool(print_overall_stats, true,
" Print the stats of the whole trace, "
"like total requests, keys, and etc.");
DEFINE_bool(output_key_distribution, false, "Print the key size distribution.");
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
DEFINE_bool(
output_value_distribution, false,
"Out put the value size distribution, only available for Put and Merge.\n"
"File name: <prefix>-<query_type>-<cf_id>"
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
"-accessed_value_size_distribution.txt\n"
"Format:[Number_of_value_size_between x and "
"x+value_interval is: <the count>]");
DEFINE_int32(print_top_k_access, 1,
"<top K of the variables to be printed> "
"Print the top k accessed keys, top k accessed prefix "
"and etc.");
DEFINE_int32(output_ignore_count, 0,
"<threshold>, ignores the access count <= this value, "
"it will shorter the output.");
DEFINE_int32(value_interval, 8,
"To output the value distribution, we need to set the value "
"intervals and make the statistic of the value size distribution "
"in different intervals. The default is 8.");
DEFINE_double(sample_ratio, 1.0,
"If the trace size is extremely huge or user want to sample "
"the trace when analyzing, sample ratio can be set (0, 1.0]");
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
namespace rocksdb {
std::map<std::string, int> taOptToIndex = {
{"get", 0}, {"put", 1},
{"delete", 2}, {"single_delete", 3},
{"range_delete", 4}, {"merge", 5},
{"iterator_Seek", 6}, {"iterator_SeekForPrev", 7}};
std::map<int, std::string> taIndexToOpt = {
{0, "get"}, {1, "put"},
{2, "delete"}, {3, "single_delete"},
{4, "range_delete"}, {5, "merge"},
{6, "iterator_Seek"}, {7, "iterator_SeekForPrev"}};
namespace {
uint64_t MultiplyCheckOverflow(uint64_t op1, uint64_t op2) {
if (op1 == 0 || op2 == 0) {
return 0;
}
if (port::kMaxUint64 / op1 < op2) {
return op1;
}
return (op1 * op2);
}
void DecodeCFAndKeyFromString(std::string& buffer, uint32_t* cf_id, Slice* key) {
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
Slice buf(buffer);
GetFixed32(&buf, cf_id);
GetLengthPrefixedSlice(&buf, key);
}
} // namespace
// The default constructor of AnalyzerOptions
AnalyzerOptions::AnalyzerOptions()
: correlation_map(kTaTypeNum, std::vector<int>(kTaTypeNum, -1)) {}
AnalyzerOptions::~AnalyzerOptions() {}
void AnalyzerOptions::SparseCorrelationInput(const std::string& in_str) {
std::string cur = in_str;
if (cur.size() == 0) {
return;
}
while (!cur.empty()) {
if (cur.compare(0, 1, "[") != 0) {
fprintf(stderr, "Invalid correlation input: %s\n", in_str.c_str());
exit(1);
}
std::string opt1, opt2;
std::size_t split = cur.find_first_of(",");
if (split != std::string::npos) {
opt1 = cur.substr(1, split - 1);
} else {
fprintf(stderr, "Invalid correlation input: %s\n", in_str.c_str());
exit(1);
}
std::size_t end = cur.find_first_of("]");
if (end != std::string::npos) {
opt2 = cur.substr(split + 1, end - split - 1);
} else {
fprintf(stderr, "Invalid correlation input: %s\n", in_str.c_str());
exit(1);
}
cur = cur.substr(end + 1);
if (taOptToIndex.find(opt1) != taOptToIndex.end() &&
taOptToIndex.find(opt2) != taOptToIndex.end()) {
correlation_list.push_back(
std::make_pair(taOptToIndex[opt1], taOptToIndex[opt2]));
} else {
fprintf(stderr, "Invalid correlation input: %s\n", in_str.c_str());
exit(1);
}
}
int sequence = 0;
for (auto& it : correlation_list) {
correlation_map[it.first][it.second] = sequence;
sequence++;
}
return;
}
// The trace statistic struct constructor
TraceStats::TraceStats() {
cf_id = 0;
cf_name = "0";
a_count = 0;
a_key_id = 0;
a_key_size_sqsum = 0;
a_key_size_sum = 0;
a_key_mid = 0;
a_value_size_sqsum = 0;
a_value_size_sum = 0;
a_value_mid = 0;
a_peak_qps = 0;
a_ave_qps = 0.0;
}
TraceStats::~TraceStats() {}
// The trace analyzer constructor
TraceAnalyzer::TraceAnalyzer(std::string& trace_path, std::string& output_path,
AnalyzerOptions _analyzer_opts)
: trace_name_(trace_path),
output_path_(output_path),
analyzer_opts_(_analyzer_opts) {
rocksdb::EnvOptions env_options;
env_ = rocksdb::Env::Default();
offset_ = 0;
c_time_ = 0;
total_requests_ = 0;
total_access_keys_ = 0;
total_gets_ = 0;
total_writes_ = 0;
trace_create_time_ = 0;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
begin_time_ = 0;
end_time_ = 0;
time_series_start_ = 0;
cur_time_sec_ = 0;
if (FLAGS_sample_ratio > 1.0 || FLAGS_sample_ratio <= 0) {
sample_max_ = 1;
} else {
sample_max_ = static_cast<uint32_t>(1.0 / FLAGS_sample_ratio);
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
ta_.resize(kTaTypeNum);
ta_[0].type_name = "get";
if (FLAGS_analyze_get) {
ta_[0].enabled = true;
} else {
ta_[0].enabled = false;
}
ta_[1].type_name = "put";
if (FLAGS_analyze_put) {
ta_[1].enabled = true;
} else {
ta_[1].enabled = false;
}
ta_[2].type_name = "delete";
if (FLAGS_analyze_delete) {
ta_[2].enabled = true;
} else {
ta_[2].enabled = false;
}
ta_[3].type_name = "single_delete";
if (FLAGS_analyze_single_delete) {
ta_[3].enabled = true;
} else {
ta_[3].enabled = false;
}
ta_[4].type_name = "range_delete";
if (FLAGS_analyze_range_delete) {
ta_[4].enabled = true;
} else {
ta_[4].enabled = false;
}
ta_[5].type_name = "merge";
if (FLAGS_analyze_merge) {
ta_[5].enabled = true;
} else {
ta_[5].enabled = false;
}
ta_[6].type_name = "iterator_Seek";
if (FLAGS_analyze_iterator) {
ta_[6].enabled = true;
} else {
ta_[6].enabled = false;
}
ta_[7].type_name = "iterator_SeekForPrev";
if (FLAGS_analyze_iterator) {
ta_[7].enabled = true;
} else {
ta_[7].enabled = false;
}
for (int i = 0; i < kTaTypeNum; i++) {
ta_[i].sample_count = 0;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
}
TraceAnalyzer::~TraceAnalyzer() {}
// Prepare the processing
// Initiate the global trace reader and writer here
Status TraceAnalyzer::PrepareProcessing() {
Status s;
// Prepare the trace reader
s = NewFileTraceReader(env_, env_options_, trace_name_, &trace_reader_);
if (!s.ok()) {
return s;
}
// Prepare and open the trace sequence file writer if needed
if (FLAGS_convert_to_human_readable_trace) {
std::string trace_sequence_name;
trace_sequence_name =
output_path_ + "/" + FLAGS_output_prefix + "-human_readable_trace.txt";
s = env_->NewWritableFile(trace_sequence_name, &trace_sequence_f_,
env_options_);
if (!s.ok()) {
return s;
}
}
// prepare the general QPS file writer
if (FLAGS_output_qps_stats) {
std::string qps_stats_name;
qps_stats_name =
output_path_ + "/" + FLAGS_output_prefix + "-qps_stats.txt";
s = env_->NewWritableFile(qps_stats_name, &qps_f_, env_options_);
if (!s.ok()) {
return s;
}
qps_stats_name =
output_path_ + "/" + FLAGS_output_prefix + "-cf_qps_stats.txt";
s = env_->NewWritableFile(qps_stats_name, &cf_qps_f_, env_options_);
if (!s.ok()) {
return s;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
}
return Status::OK();
}
Status TraceAnalyzer::ReadTraceHeader(Trace* header) {
assert(header != nullptr);
Status s = ReadTraceRecord(header);
if (!s.ok()) {
return s;
}
if (header->type != kTraceBegin) {
return Status::Corruption("Corrupted trace file. Incorrect header.");
}
if (header->payload.substr(0, kTraceMagic.length()) != kTraceMagic) {
return Status::Corruption("Corrupted trace file. Incorrect magic.");
}
return s;
}
Status TraceAnalyzer::ReadTraceFooter(Trace* footer) {
assert(footer != nullptr);
Status s = ReadTraceRecord(footer);
if (!s.ok()) {
return s;
}
if (footer->type != kTraceEnd) {
return Status::Corruption("Corrupted trace file. Incorrect footer.");
}
return s;
}
Status TraceAnalyzer::ReadTraceRecord(Trace* trace) {
assert(trace != nullptr);
std::string encoded_trace;
Status s = trace_reader_->Read(&encoded_trace);
if (!s.ok()) {
return s;
}
Slice enc_slice = Slice(encoded_trace);
GetFixed64(&enc_slice, &trace->ts);
trace->type = static_cast<TraceType>(enc_slice[0]);
enc_slice.remove_prefix(kTraceTypeSize + kTracePayloadLengthSize);
trace->payload = enc_slice.ToString();
return s;
}
// process the trace itself and redirect the trace content
// to different operation type handler. With different race
// format, this function can be changed
Status TraceAnalyzer::StartProcessing() {
Status s;
Trace header;
s = ReadTraceHeader(&header);
if (!s.ok()) {
fprintf(stderr, "Cannot read the header\n");
return s;
}
trace_create_time_ = header.ts;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (FLAGS_output_time_series) {
time_series_start_ = header.ts;
}
Trace trace;
while (s.ok()) {
trace.reset();
s = ReadTraceRecord(&trace);
if (!s.ok()) {
break;
}
total_requests_++;
end_time_ = trace.ts;
if (trace.type == kTraceWrite) {
total_writes_++;
c_time_ = trace.ts;
WriteBatch batch(trace.payload);
// Note that, if the write happens in a transaction,
// 'Write' will be called twice, one for Prepare, one for
// Commit. Thus, in the trace, for the same WriteBatch, there
// will be two reords if it is in a transaction. Here, we only
// process the reord that is committed. If write is non-transaction,
// HasBeginPrepare()==false, so we process it normally.
if (batch.HasBeginPrepare() && !batch.HasCommit()) {
continue;
}
TraceWriteHandler write_handler(this);
s = batch.Iterate(&write_handler);
if (!s.ok()) {
fprintf(stderr, "Cannot process the write batch in the trace\n");
return s;
}
} else if (trace.type == kTraceGet) {
uint32_t cf_id = 0;
Slice key;
DecodeCFAndKeyFromString(trace.payload, &cf_id, &key);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
total_gets_++;
s = HandleGet(cf_id, key.ToString(), trace.ts, 1);
if (!s.ok()) {
fprintf(stderr, "Cannot process the get in the trace\n");
return s;
}
} else if (trace.type == kTraceIteratorSeek ||
trace.type == kTraceIteratorSeekForPrev) {
uint32_t cf_id = 0;
Slice key;
DecodeCFAndKeyFromString(trace.payload, &cf_id, &key);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
s = HandleIter(cf_id, key.ToString(), trace.ts, trace.type);
if (!s.ok()) {
fprintf(stderr, "Cannot process the iterator in the trace\n");
return s;
}
} else if (trace.type == kTraceEnd) {
break;
}
}
if (s.IsIncomplete()) {
// Fix it: Reaching eof returns Incomplete status at the moment.
//
return Status::OK();
}
return s;
}
// After the trace is processed by StartProcessing, the statistic data
// is stored in the map or other in memory data structures. To get the
// other statistic result such as key size distribution, value size
// distribution, these data structures are re-processed here.
Status TraceAnalyzer::MakeStatistics() {
int ret;
Status s;
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
for (auto& stat : ta_[type].stats) {
stat.second.a_key_id = 0;
for (auto& record : stat.second.a_key_stats) {
record.second.key_id = stat.second.a_key_id;
stat.second.a_key_id++;
if (record.second.access_count <=
static_cast<uint64_t>(FLAGS_output_ignore_count)) {
continue;
}
// Generate the key access count distribution data
if (FLAGS_output_access_count_stats) {
if (stat.second.a_count_stats.find(record.second.access_count) ==
stat.second.a_count_stats.end()) {
stat.second.a_count_stats[record.second.access_count] = 1;
} else {
stat.second.a_count_stats[record.second.access_count]++;
}
}
// Generate the key size distribution data
if (FLAGS_output_key_distribution) {
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (stat.second.a_key_size_stats.find(record.first.size()) ==
stat.second.a_key_size_stats.end()) {
stat.second.a_key_size_stats[record.first.size()] = 1;
} else {
stat.second.a_key_size_stats[record.first.size()]++;
}
}
if (!FLAGS_print_correlation.empty()) {
s = MakeStatisticCorrelation(stat.second, record.second);
if (!s.ok()) {
return s;
}
}
}
// Output the prefix cut or the whole content of the accessed key space
if (FLAGS_output_key_stats || FLAGS_output_prefix_cut > 0) {
s = MakeStatisticKeyStatsOrPrefix(stat.second);
if (!s.ok()) {
return s;
}
}
// output the access count distribution
if (FLAGS_output_access_count_stats && stat.second.a_count_dist_f) {
for (auto& record : stat.second.a_count_stats) {
ret = sprintf(buffer_, "access_count: %" PRIu64 " num: %" PRIu64 "\n",
record.first, record.second);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.second.a_count_dist_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write access count distribution file failed\n");
return s;
}
}
}
// find the medium of the key size
uint64_t k_count = 0;
bool get_mid = false;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
for (auto& record : stat.second.a_key_size_stats) {
k_count += record.second;
if (!get_mid && k_count >= stat.second.a_key_mid) {
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
stat.second.a_key_mid = record.first;
get_mid = true;
}
if (FLAGS_output_key_distribution && stat.second.a_key_size_f) {
ret = sprintf(buffer_, "%" PRIu64 " %" PRIu64 "\n", record.first,
record.second);
if (ret < 0) {
return Status::IOError("Format output failed");
}
std::string printout(buffer_);
s = stat.second.a_key_size_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write key size distribution file failed\n");
return s;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
}
}
// output the value size distribution
uint64_t v_begin = 0, v_end = 0, v_count = 0;
get_mid = false;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
for (auto& record : stat.second.a_value_size_stats) {
v_begin = v_end;
v_end = (record.first + 1) * FLAGS_value_interval;
v_count += record.second;
if (!get_mid && v_count >= stat.second.a_count / 2) {
stat.second.a_value_mid = (v_begin + v_end) / 2;
get_mid = true;
}
if (FLAGS_output_value_distribution && stat.second.a_value_size_f &&
(type == TraceOperationType::kPut ||
type == TraceOperationType::kMerge)) {
ret = sprintf(buffer_,
"Number_of_value_size_between %" PRIu64 " and %" PRIu64
" is: %" PRIu64 "\n",
v_begin, v_end, record.second);
if (ret < 0) {
return Status::IOError("Format output failed");
}
std::string printout(buffer_);
s = stat.second.a_value_size_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write value size distribution file failed\n");
return s;
}
}
}
}
}
// Make the QPS statistics
if (FLAGS_output_qps_stats) {
s = MakeStatisticQPS();
if (!s.ok()) {
return s;
}
}
return Status::OK();
}
// Process the statistics of the key access and
// prefix of the accessed keys if required
Status TraceAnalyzer::MakeStatisticKeyStatsOrPrefix(TraceStats& stats) {
int ret;
Status s;
std::string prefix = "0";
uint64_t prefix_access = 0;
uint64_t prefix_count = 0;
uint64_t prefix_succ_access = 0;
double prefix_ave_access = 0.0;
stats.a_succ_count = 0;
for (auto& record : stats.a_key_stats) {
// write the key access statistic file
if (!stats.a_key_f) {
return Status::IOError("Failed to open accessed_key_stats file.");
}
stats.a_succ_count += record.second.succ_count;
double succ_ratio = 0.0;
if (record.second.access_count > 0) {
succ_ratio = (static_cast<double>(record.second.succ_count)) /
record.second.access_count;
}
ret = sprintf(buffer_, "%u %zu %" PRIu64 " %" PRIu64 " %f\n",
record.second.cf_id, record.second.value_size,
record.second.key_id, record.second.access_count, succ_ratio);
if (ret < 0) {
return Status::IOError("Format output failed");
}
std::string printout(buffer_);
s = stats.a_key_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write key access file failed\n");
return s;
}
// write the prefix cut of the accessed keys
if (FLAGS_output_prefix_cut > 0 && stats.a_prefix_cut_f) {
if (record.first.compare(0, FLAGS_output_prefix_cut, prefix) != 0) {
std::string prefix_out = rocksdb::LDBCommand::StringToHex(prefix);
if (prefix_count == 0) {
prefix_ave_access = 0.0;
} else {
prefix_ave_access =
(static_cast<double>(prefix_access)) / prefix_count;
}
double prefix_succ_ratio = 0.0;
if (prefix_access > 0) {
prefix_succ_ratio =
(static_cast<double>(prefix_succ_access)) / prefix_access;
}
ret = sprintf(buffer_, "%" PRIu64 " %" PRIu64 " %" PRIu64 " %f %f %s\n",
record.second.key_id, prefix_access, prefix_count,
prefix_ave_access, prefix_succ_ratio, prefix_out.c_str());
if (ret < 0) {
return Status::IOError("Format output failed");
}
std::string pout(buffer_);
s = stats.a_prefix_cut_f->Append(pout);
if (!s.ok()) {
fprintf(stderr, "Write accessed key prefix file failed\n");
return s;
}
// make the top k statistic for the prefix
if (static_cast<int32_t>(stats.top_k_prefix_access.size()) <
FLAGS_print_top_k_access) {
stats.top_k_prefix_access.push(
std::make_pair(prefix_access, prefix_out));
} else {
if (prefix_access > stats.top_k_prefix_access.top().first) {
stats.top_k_prefix_access.pop();
stats.top_k_prefix_access.push(
std::make_pair(prefix_access, prefix_out));
}
}
if (static_cast<int32_t>(stats.top_k_prefix_ave.size()) <
FLAGS_print_top_k_access) {
stats.top_k_prefix_ave.push(
std::make_pair(prefix_ave_access, prefix_out));
} else {
if (prefix_ave_access > stats.top_k_prefix_ave.top().first) {
stats.top_k_prefix_ave.pop();
stats.top_k_prefix_ave.push(
std::make_pair(prefix_ave_access, prefix_out));
}
}
prefix = record.first.substr(0, FLAGS_output_prefix_cut);
prefix_access = 0;
prefix_count = 0;
prefix_succ_access = 0;
}
prefix_access += record.second.access_count;
prefix_count += 1;
prefix_succ_access += record.second.succ_count;
}
}
return Status::OK();
}
// Process the statistics of different query type
// correlations
Status TraceAnalyzer::MakeStatisticCorrelation(TraceStats& stats,
StatsUnit& unit) {
if (stats.correlation_output.size() !=
analyzer_opts_.correlation_list.size()) {
return Status::Corruption("Cannot make the statistic of correlation.");
}
for (int i = 0; i < static_cast<int>(analyzer_opts_.correlation_list.size());
i++) {
if (i >= static_cast<int>(stats.correlation_output.size()) ||
i >= static_cast<int>(unit.v_correlation.size())) {
break;
}
stats.correlation_output[i].first += unit.v_correlation[i].count;
stats.correlation_output[i].second += unit.v_correlation[i].total_ts;
}
return Status::OK();
}
// Process the statistics of QPS
Status TraceAnalyzer::MakeStatisticQPS() {
if(begin_time_ == 0) {
begin_time_ = trace_create_time_;
}
uint32_t duration =
static_cast<uint32_t>((end_time_ - begin_time_) / 1000000);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
int ret;
Status s;
std::vector<std::vector<uint32_t>> type_qps(
duration, std::vector<uint32_t>(kTaTypeNum + 1, 0));
std::vector<uint64_t> qps_sum(kTaTypeNum + 1, 0);
std::vector<uint32_t> qps_peak(kTaTypeNum + 1, 0);
qps_ave_.resize(kTaTypeNum + 1);
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
for (auto& stat : ta_[type].stats) {
uint32_t time_line = 0;
uint64_t cf_qps_sum = 0;
for (auto& time_it : stat.second.a_qps_stats) {
if (time_it.first >= duration) {
continue;
}
type_qps[time_it.first][kTaTypeNum] += time_it.second;
type_qps[time_it.first][type] += time_it.second;
cf_qps_sum += time_it.second;
if (time_it.second > stat.second.a_peak_qps) {
stat.second.a_peak_qps = time_it.second;
}
if (stat.second.a_qps_f) {
while (time_line < time_it.first) {
ret = sprintf(buffer_, "%u\n", 0);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.second.a_qps_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write QPS file failed\n");
return s;
}
time_line++;
}
ret = sprintf(buffer_, "%u\n", time_it.second);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.second.a_qps_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write QPS file failed\n");
return s;
}
if (time_line == time_it.first) {
time_line++;
}
}
// Process the top k QPS peaks
if (FLAGS_output_prefix_cut > 0) {
if (static_cast<int32_t>(stat.second.top_k_qps_sec.size()) <
FLAGS_print_top_k_access) {
stat.second.top_k_qps_sec.push(
std::make_pair(time_it.second, time_it.first));
} else {
if (stat.second.top_k_qps_sec.size() > 0 &&
stat.second.top_k_qps_sec.top().first < time_it.second) {
stat.second.top_k_qps_sec.pop();
stat.second.top_k_qps_sec.push(
std::make_pair(time_it.second, time_it.first));
}
}
}
}
if (duration == 0) {
stat.second.a_ave_qps = 0;
} else {
stat.second.a_ave_qps = (static_cast<double>(cf_qps_sum)) / duration;
}
// Output the accessed unique key number change overtime
if (stat.second.a_key_num_f) {
uint64_t cur_uni_key =
static_cast<uint64_t>(stat.second.a_key_stats.size());
double cur_ratio = 0.0;
uint64_t cur_num = 0;
for (uint32_t i = 0; i < duration; i++) {
auto find_time = stat.second.uni_key_num.find(i);
if (find_time != stat.second.uni_key_num.end()) {
cur_ratio = (static_cast<double>(find_time->second)) / cur_uni_key;
cur_num = find_time->second;
}
ret = sprintf(buffer_, "%" PRIu64 " %.12f\n", cur_num, cur_ratio);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.second.a_key_num_f->Append(printout);
if (!s.ok()) {
fprintf(stderr,
"Write accessed unique key number change file failed\n");
return s;
}
}
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
// output the prefix of top k access peak
if (FLAGS_output_prefix_cut > 0 && stat.second.a_top_qps_prefix_f) {
while (!stat.second.top_k_qps_sec.empty()) {
ret = sprintf(buffer_, "At time: %u with QPS: %u\n",
stat.second.top_k_qps_sec.top().second,
stat.second.top_k_qps_sec.top().first);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.second.a_top_qps_prefix_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write prefix QPS top K file failed\n");
return s;
}
uint32_t qps_time = stat.second.top_k_qps_sec.top().second;
stat.second.top_k_qps_sec.pop();
if (stat.second.a_qps_prefix_stats.find(qps_time) !=
stat.second.a_qps_prefix_stats.end()) {
for (auto& qps_prefix : stat.second.a_qps_prefix_stats[qps_time]) {
std::string qps_prefix_out =
rocksdb::LDBCommand::StringToHex(qps_prefix.first);
ret = sprintf(buffer_, "The prefix: %s Access count: %u\n",
qps_prefix_out.c_str(), qps_prefix.second);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string pout(buffer_);
s = stat.second.a_top_qps_prefix_f->Append(pout);
if (!s.ok()) {
fprintf(stderr, "Write prefix QPS top K file failed\n");
return s;
}
}
}
}
}
}
}
if (qps_f_) {
for (uint32_t i = 0; i < duration; i++) {
for (int type = 0; type <= kTaTypeNum; type++) {
if (type < kTaTypeNum) {
ret = sprintf(buffer_, "%u ", type_qps[i][type]);
} else {
ret = sprintf(buffer_, "%u\n", type_qps[i][type]);
}
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = qps_f_->Append(printout);
if (!s.ok()) {
return s;
}
qps_sum[type] += type_qps[i][type];
if (type_qps[i][type] > qps_peak[type]) {
qps_peak[type] = type_qps[i][type];
}
}
}
}
if (cf_qps_f_) {
int cfs_size = static_cast<uint32_t>(cfs_.size());
uint32_t v;
for (uint32_t i = 0; i < duration; i++) {
for (int cf = 0; cf < cfs_size; cf++) {
if (cfs_[cf].cf_qps.find(i) != cfs_[cf].cf_qps.end()) {
v = cfs_[cf].cf_qps[i];
} else {
v = 0;
}
if (cf < cfs_size - 1) {
ret = sprintf(buffer_, "%u ", v);
} else {
ret = sprintf(buffer_, "%u\n", v);
}
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = cf_qps_f_->Append(printout);
if (!s.ok()) {
return s;
}
}
}
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
qps_peak_ = qps_peak;
for (int type = 0; type <= kTaTypeNum; type++) {
if (duration == 0) {
qps_ave_[type] = 0;
} else {
qps_ave_[type] = (static_cast<double>(qps_sum[type])) / duration;
}
}
return Status::OK();
}
// In reprocessing, if we have the whole key space
// we can output the access count of all keys in a cf
// we can make some statistics of the whole key space
// also, we output the top k accessed keys here
Status TraceAnalyzer::ReProcessing() {
int ret;
Status s;
for (auto& cf_it : cfs_) {
uint32_t cf_id = cf_it.first;
// output the time series;
if (FLAGS_output_time_series) {
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled ||
ta_[type].stats.find(cf_id) == ta_[type].stats.end()) {
continue;
}
TraceStats& stat = ta_[type].stats[cf_id];
if (!stat.time_series_f) {
fprintf(stderr, "Cannot write time_series of '%s' in '%u'\n",
ta_[type].type_name.c_str(), cf_id);
continue;
}
while (!stat.time_series.empty()) {
uint64_t key_id = 0;
auto found = stat.a_key_stats.find(stat.time_series.front().key);
if (found != stat.a_key_stats.end()) {
key_id = found->second.key_id;
}
ret = sprintf(buffer_, "%u %" PRIu64 " %" PRIu64 "\n",
stat.time_series.front().type,
stat.time_series.front().ts, key_id);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.time_series_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write time series file failed\n");
return s;
}
stat.time_series.pop_front();
}
}
}
// process the whole key space if needed
if (!FLAGS_key_space_dir.empty()) {
std::string whole_key_path =
FLAGS_key_space_dir + "/" + std::to_string(cf_id) + ".txt";
std::string input_key, get_key;
std::vector<std::string> prefix(kTaTypeNum);
std::istringstream iss;
bool has_data = true;
s = env_->NewSequentialFile(whole_key_path, &wkey_input_f_, env_options_);
if (!s.ok()) {
fprintf(stderr, "Cannot open the whole key space file of CF: %u\n",
cf_id);
wkey_input_f_.reset();
}
if (wkey_input_f_) {
for (cfs_[cf_id].w_count = 0;
ReadOneLine(&iss, wkey_input_f_.get(), &get_key, &has_data, &s);
++cfs_[cf_id].w_count) {
if (!s.ok()) {
fprintf(stderr, "Read whole key space file failed\n");
return s;
}
input_key = rocksdb::LDBCommand::HexToString(get_key);
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
TraceStats& stat = ta_[type].stats[cf_id];
if (stat.w_key_f) {
if (stat.a_key_stats.find(input_key) != stat.a_key_stats.end()) {
ret = sprintf(buffer_, "%" PRIu64 " %" PRIu64 "\n",
cfs_[cf_id].w_count,
stat.a_key_stats[input_key].access_count);
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.w_key_f->Append(printout);
if (!s.ok()) {
fprintf(stderr, "Write whole key space access file failed\n");
return s;
}
}
}
// Output the prefix cut file of the whole key space
if (FLAGS_output_prefix_cut > 0 && stat.w_prefix_cut_f) {
if (input_key.compare(0, FLAGS_output_prefix_cut, prefix[type]) !=
0) {
prefix[type] = input_key.substr(0, FLAGS_output_prefix_cut);
std::string prefix_out =
rocksdb::LDBCommand::StringToHex(prefix[type]);
ret = sprintf(buffer_, "%" PRIu64 " %s\n", cfs_[cf_id].w_count,
prefix_out.c_str());
if (ret < 0) {
return Status::IOError("Format the output failed");
}
std::string printout(buffer_);
s = stat.w_prefix_cut_f->Append(printout);
if (!s.ok()) {
fprintf(stderr,
"Write whole key space prefix cut file failed\n");
return s;
}
}
}
}
// Make the statistics fo the key size distribution
if (FLAGS_output_key_distribution) {
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (cfs_[cf_id].w_key_size_stats.find(input_key.size()) ==
cfs_[cf_id].w_key_size_stats.end()) {
cfs_[cf_id].w_key_size_stats[input_key.size()] = 1;
} else {
cfs_[cf_id].w_key_size_stats[input_key.size()]++;
}
}
}
}
}
// process the top k accessed keys
if (FLAGS_print_top_k_access > 0) {
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled ||
ta_[type].stats.find(cf_id) == ta_[type].stats.end()) {
continue;
}
TraceStats& stat = ta_[type].stats[cf_id];
for (auto& record : stat.a_key_stats) {
if (static_cast<int32_t>(stat.top_k_queue.size()) <
FLAGS_print_top_k_access) {
stat.top_k_queue.push(
std::make_pair(record.second.access_count, record.first));
} else {
if (record.second.access_count > stat.top_k_queue.top().first) {
stat.top_k_queue.pop();
stat.top_k_queue.push(
std::make_pair(record.second.access_count, record.first));
}
}
}
}
}
}
return Status::OK();
}
// End the processing, print the requested results
Status TraceAnalyzer::EndProcessing() {
if (trace_sequence_f_) {
trace_sequence_f_->Close();
}
if (FLAGS_no_print) {
return Status::OK();
}
PrintStatistics();
CloseOutputFiles();
return Status::OK();
}
// Insert the corresponding key statistics to the correct type
// and correct CF, output the time-series file if needed
Status TraceAnalyzer::KeyStatsInsertion(const uint32_t& type,
const uint32_t& cf_id,
const std::string& key,
const size_t value_size,
const uint64_t ts) {
Status s;
StatsUnit unit;
unit.key_id = 0;
unit.cf_id = cf_id;
unit.value_size = value_size;
unit.access_count = 1;
unit.latest_ts = ts;
if (type != TraceOperationType::kGet || value_size > 0) {
unit.succ_count = 1;
} else {
unit.succ_count = 0;
}
unit.v_correlation.resize(analyzer_opts_.correlation_list.size());
for (int i = 0;
i < (static_cast<int>(analyzer_opts_.correlation_list.size())); i++) {
unit.v_correlation[i].count = 0;
unit.v_correlation[i].total_ts = 0;
}
std::string prefix;
if (FLAGS_output_prefix_cut > 0) {
prefix = key.substr(0, FLAGS_output_prefix_cut);
}
if (begin_time_ == 0) {
begin_time_ = ts;
}
uint32_t time_in_sec;
if (ts < begin_time_) {
time_in_sec = 0;
} else {
time_in_sec = static_cast<uint32_t>((ts - begin_time_) / 1000000);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
}
uint64_t dist_value_size = value_size / FLAGS_value_interval;
auto found_stats = ta_[type].stats.find(cf_id);
if (found_stats == ta_[type].stats.end()) {
ta_[type].stats[cf_id].cf_id = cf_id;
ta_[type].stats[cf_id].cf_name = std::to_string(cf_id);
ta_[type].stats[cf_id].a_count = 1;
ta_[type].stats[cf_id].a_key_id = 0;
ta_[type].stats[cf_id].a_key_size_sqsum = MultiplyCheckOverflow(
static_cast<uint64_t>(key.size()), static_cast<uint64_t>(key.size()));
ta_[type].stats[cf_id].a_key_size_sum = key.size();
ta_[type].stats[cf_id].a_value_size_sqsum = MultiplyCheckOverflow(
static_cast<uint64_t>(value_size), static_cast<uint64_t>(value_size));
ta_[type].stats[cf_id].a_value_size_sum = value_size;
s = OpenStatsOutputFiles(ta_[type].type_name, ta_[type].stats[cf_id]);
if (!FLAGS_print_correlation.empty()) {
s = StatsUnitCorrelationUpdate(unit, type, ts, key);
}
ta_[type].stats[cf_id].a_key_stats[key] = unit;
ta_[type].stats[cf_id].a_value_size_stats[dist_value_size] = 1;
ta_[type].stats[cf_id].a_qps_stats[time_in_sec] = 1;
ta_[type].stats[cf_id].correlation_output.resize(
analyzer_opts_.correlation_list.size());
if (FLAGS_output_prefix_cut > 0) {
std::map<std::string, uint32_t> tmp_qps_map;
tmp_qps_map[prefix] = 1;
ta_[type].stats[cf_id].a_qps_prefix_stats[time_in_sec] = tmp_qps_map;
}
if (time_in_sec != cur_time_sec_) {
ta_[type].stats[cf_id].uni_key_num[cur_time_sec_] =
static_cast<uint64_t>(ta_[type].stats[cf_id].a_key_stats.size());
cur_time_sec_ = time_in_sec;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
} else {
found_stats->second.a_count++;
found_stats->second.a_key_size_sqsum += MultiplyCheckOverflow(
static_cast<uint64_t>(key.size()), static_cast<uint64_t>(key.size()));
found_stats->second.a_key_size_sum += key.size();
found_stats->second.a_value_size_sqsum += MultiplyCheckOverflow(
static_cast<uint64_t>(value_size), static_cast<uint64_t>(value_size));
found_stats->second.a_value_size_sum += value_size;
auto found_key = found_stats->second.a_key_stats.find(key);
if (found_key == found_stats->second.a_key_stats.end()) {
found_stats->second.a_key_stats[key] = unit;
} else {
found_key->second.access_count++;
if (type != TraceOperationType::kGet || value_size > 0) {
found_key->second.succ_count++;
}
if (!FLAGS_print_correlation.empty()) {
s = StatsUnitCorrelationUpdate(found_key->second, type, ts, key);
}
}
if (time_in_sec != cur_time_sec_) {
found_stats->second.uni_key_num[cur_time_sec_] =
static_cast<uint64_t>(found_stats->second.a_key_stats.size());
cur_time_sec_ = time_in_sec;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
auto found_value =
found_stats->second.a_value_size_stats.find(dist_value_size);
if (found_value == found_stats->second.a_value_size_stats.end()) {
found_stats->second.a_value_size_stats[dist_value_size] = 1;
} else {
found_value->second++;
}
auto found_qps = found_stats->second.a_qps_stats.find(time_in_sec);
if (found_qps == found_stats->second.a_qps_stats.end()) {
found_stats->second.a_qps_stats[time_in_sec] = 1;
} else {
found_qps->second++;
}
if (FLAGS_output_prefix_cut > 0) {
auto found_qps_prefix =
found_stats->second.a_qps_prefix_stats.find(time_in_sec);
if (found_qps_prefix == found_stats->second.a_qps_prefix_stats.end()) {
std::map<std::string, uint32_t> tmp_qps_map;
found_stats->second.a_qps_prefix_stats[time_in_sec] = tmp_qps_map;
}
if (found_stats->second.a_qps_prefix_stats[time_in_sec].find(prefix) ==
found_stats->second.a_qps_prefix_stats[time_in_sec].end()) {
found_stats->second.a_qps_prefix_stats[time_in_sec][prefix] = 1;
} else {
found_stats->second.a_qps_prefix_stats[time_in_sec][prefix]++;
}
}
}
if (cfs_.find(cf_id) == cfs_.end()) {
CfUnit cf_unit;
cf_unit.cf_id = cf_id;
cf_unit.w_count = 0;
cf_unit.a_count = 0;
cfs_[cf_id] = cf_unit;
}
if (FLAGS_output_qps_stats) {
cfs_[cf_id].cf_qps[time_in_sec]++;
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (FLAGS_output_time_series) {
TraceUnit trace_u;
trace_u.type = type;
trace_u.key = key;
trace_u.value_size = value_size;
trace_u.ts = (ts - time_series_start_) / 1000000;
trace_u.cf_id = cf_id;
ta_[type].stats[cf_id].time_series.push_back(trace_u);
}
return Status::OK();
}
// Update the correlation unit of each key if enabled
Status TraceAnalyzer::StatsUnitCorrelationUpdate(StatsUnit& unit,
const uint32_t& type_second,
const uint64_t& ts,
const std::string& key) {
if (type_second >= kTaTypeNum) {
fprintf(stderr, "Unknown Type Id: %u\n", type_second);
return Status::NotFound();
}
for (int type_first = 0; type_first < kTaTypeNum; type_first++) {
if (type_first >= static_cast<int>(ta_.size()) ||
type_first >= static_cast<int>(analyzer_opts_.correlation_map.size())) {
break;
}
if (analyzer_opts_.correlation_map[type_first][type_second] < 0 ||
ta_[type_first].stats.find(unit.cf_id) == ta_[type_first].stats.end() ||
ta_[type_first].stats[unit.cf_id].a_key_stats.find(key) ==
ta_[type_first].stats[unit.cf_id].a_key_stats.end() ||
ta_[type_first].stats[unit.cf_id].a_key_stats[key].latest_ts == ts) {
continue;
}
int correlation_id =
analyzer_opts_.correlation_map[type_first][type_second];
// after get the x-y operation time or x, update;
if (correlation_id < 0 ||
correlation_id >= static_cast<int>(unit.v_correlation.size())) {
continue;
}
unit.v_correlation[correlation_id].count++;
unit.v_correlation[correlation_id].total_ts +=
(ts - ta_[type_first].stats[unit.cf_id].a_key_stats[key].latest_ts);
}
unit.latest_ts = ts;
return Status::OK();
}
// when a new trace statistic is created, the file handler
// pointers should be initiated if needed according to
// the trace analyzer options
Status TraceAnalyzer::OpenStatsOutputFiles(const std::string& type,
TraceStats& new_stats) {
Status s;
if (FLAGS_output_key_stats) {
s = CreateOutputFile(type, new_stats.cf_name, "accessed_key_stats.txt",
&new_stats.a_key_f);
s = CreateOutputFile(type, new_stats.cf_name,
"accessed_unique_key_num_change.txt",
&new_stats.a_key_num_f);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!FLAGS_key_space_dir.empty()) {
s = CreateOutputFile(type, new_stats.cf_name, "whole_key_stats.txt",
&new_stats.w_key_f);
}
}
if (FLAGS_output_access_count_stats) {
s = CreateOutputFile(type, new_stats.cf_name,
"accessed_key_count_distribution.txt",
&new_stats.a_count_dist_f);
}
if (FLAGS_output_prefix_cut > 0) {
s = CreateOutputFile(type, new_stats.cf_name, "accessed_key_prefix_cut.txt",
&new_stats.a_prefix_cut_f);
if (!FLAGS_key_space_dir.empty()) {
s = CreateOutputFile(type, new_stats.cf_name, "whole_key_prefix_cut.txt",
&new_stats.w_prefix_cut_f);
}
if (FLAGS_output_qps_stats) {
s = CreateOutputFile(type, new_stats.cf_name,
"accessed_top_k_qps_prefix_cut.txt",
&new_stats.a_top_qps_prefix_f);
}
}
if (FLAGS_output_time_series) {
s = CreateOutputFile(type, new_stats.cf_name, "time_series.txt",
&new_stats.time_series_f);
}
if (FLAGS_output_value_distribution) {
s = CreateOutputFile(type, new_stats.cf_name,
"accessed_value_size_distribution.txt",
&new_stats.a_value_size_f);
}
if (FLAGS_output_key_distribution) {
s = CreateOutputFile(type, new_stats.cf_name,
"accessed_key_size_distribution.txt",
&new_stats.a_key_size_f);
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (FLAGS_output_qps_stats) {
s = CreateOutputFile(type, new_stats.cf_name, "qps_stats.txt",
&new_stats.a_qps_f);
}
return Status::OK();
}
// create the output path of the files to be opened
Status TraceAnalyzer::CreateOutputFile(
const std::string& type, const std::string& cf_name,
const std::string& ending, std::unique_ptr<rocksdb::WritableFile>* f_ptr) {
std::string path;
path = output_path_ + "/" + FLAGS_output_prefix + "-" + type + "-" + cf_name +
"-" + ending;
Status s;
s = env_->NewWritableFile(path, f_ptr, env_options_);
if (!s.ok()) {
fprintf(stderr, "Cannot open file: %s\n", path.c_str());
exit(1);
}
return Status::OK();
}
// Close the output files in the TraceStats if they are opened
void TraceAnalyzer::CloseOutputFiles() {
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
for (auto& stat : ta_[type].stats) {
if (stat.second.time_series_f) {
stat.second.time_series_f->Close();
}
if (stat.second.a_key_f) {
stat.second.a_key_f->Close();
}
if (stat.second.a_key_num_f) {
stat.second.a_key_num_f->Close();
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (stat.second.a_count_dist_f) {
stat.second.a_count_dist_f->Close();
}
if (stat.second.a_prefix_cut_f) {
stat.second.a_prefix_cut_f->Close();
}
if (stat.second.a_value_size_f) {
stat.second.a_value_size_f->Close();
}
if (stat.second.a_key_size_f) {
stat.second.a_key_size_f->Close();
}
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (stat.second.a_qps_f) {
stat.second.a_qps_f->Close();
}
if (stat.second.a_top_qps_prefix_f) {
stat.second.a_top_qps_prefix_f->Close();
}
if (stat.second.w_key_f) {
stat.second.w_key_f->Close();
}
if (stat.second.w_prefix_cut_f) {
stat.second.w_prefix_cut_f->Close();
}
}
}
return;
}
// Handle the Get request in the trace
Status TraceAnalyzer::HandleGet(uint32_t column_family_id,
const std::string& key, const uint64_t& ts,
const uint32_t& get_ret) {
Status s;
size_t value_size = 0;
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kGet, column_family_id, key,
value_size, ts);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kGet].sample_count >= sample_max_) {
ta_[TraceOperationType::kGet].sample_count = 0;
}
if (ta_[TraceOperationType::kGet].sample_count > 0) {
ta_[TraceOperationType::kGet].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kGet].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kGet].enabled) {
return Status::OK();
}
if (get_ret == 1) {
value_size = 10;
}
s = KeyStatsInsertion(TraceOperationType::kGet, column_family_id, key,
value_size, ts);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the Put request in the write batch of the trace
Status TraceAnalyzer::HandlePut(uint32_t column_family_id, const Slice& key,
const Slice& value) {
Status s;
size_t value_size = value.ToString().size();
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kPut, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kPut].sample_count >= sample_max_) {
ta_[TraceOperationType::kPut].sample_count = 0;
}
if (ta_[TraceOperationType::kPut].sample_count > 0) {
ta_[TraceOperationType::kPut].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kPut].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kPut].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(TraceOperationType::kPut, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the Delete request in the write batch of the trace
Status TraceAnalyzer::HandleDelete(uint32_t column_family_id,
const Slice& key) {
Status s;
size_t value_size = 0;
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kDelete, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kDelete].sample_count >= sample_max_) {
ta_[TraceOperationType::kDelete].sample_count = 0;
}
if (ta_[TraceOperationType::kDelete].sample_count > 0) {
ta_[TraceOperationType::kDelete].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kDelete].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kDelete].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(TraceOperationType::kDelete, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the SingleDelete request in the write batch of the trace
Status TraceAnalyzer::HandleSingleDelete(uint32_t column_family_id,
const Slice& key) {
Status s;
size_t value_size = 0;
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kSingleDelete, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kSingleDelete].sample_count >= sample_max_) {
ta_[TraceOperationType::kSingleDelete].sample_count = 0;
}
if (ta_[TraceOperationType::kSingleDelete].sample_count > 0) {
ta_[TraceOperationType::kSingleDelete].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kSingleDelete].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kSingleDelete].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(TraceOperationType::kSingleDelete, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the DeleteRange request in the write batch of the trace
Status TraceAnalyzer::HandleDeleteRange(uint32_t column_family_id,
const Slice& begin_key,
const Slice& end_key) {
Status s;
size_t value_size = 0;
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kRangeDelete, column_family_id,
begin_key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kRangeDelete].sample_count >= sample_max_) {
ta_[TraceOperationType::kRangeDelete].sample_count = 0;
}
if (ta_[TraceOperationType::kRangeDelete].sample_count > 0) {
ta_[TraceOperationType::kRangeDelete].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kRangeDelete].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kRangeDelete].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(TraceOperationType::kRangeDelete, column_family_id,
begin_key.ToString(), value_size, c_time_);
s = KeyStatsInsertion(TraceOperationType::kRangeDelete, column_family_id,
end_key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the Merge request in the write batch of the trace
Status TraceAnalyzer::HandleMerge(uint32_t column_family_id, const Slice& key,
const Slice& value) {
Status s;
size_t value_size = value.ToString().size();
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(TraceOperationType::kMerge, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[TraceOperationType::kMerge].sample_count >= sample_max_) {
ta_[TraceOperationType::kMerge].sample_count = 0;
}
if (ta_[TraceOperationType::kMerge].sample_count > 0) {
ta_[TraceOperationType::kMerge].sample_count++;
return Status::OK();
}
ta_[TraceOperationType::kMerge].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[TraceOperationType::kMerge].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(TraceOperationType::kMerge, column_family_id,
key.ToString(), value_size, c_time_);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Handle the Iterator request in the trace
Status TraceAnalyzer::HandleIter(uint32_t column_family_id,
const std::string& key, const uint64_t& ts,
TraceType& trace_type) {
Status s;
size_t value_size = 0;
int type = -1;
if (trace_type == kTraceIteratorSeek) {
type = TraceOperationType::kIteratorSeek;
} else if (trace_type == kTraceIteratorSeekForPrev) {
type = TraceOperationType::kIteratorSeekForPrev;
} else {
return s;
}
if (type == -1) {
return s;
}
if (FLAGS_convert_to_human_readable_trace && trace_sequence_f_) {
s = WriteTraceSequence(type, column_family_id, key, value_size, ts);
if (!s.ok()) {
return Status::Corruption("Failed to write the trace sequence to file");
}
}
if (ta_[type].sample_count >= sample_max_) {
ta_[type].sample_count = 0;
}
if (ta_[type].sample_count > 0) {
ta_[type].sample_count++;
return Status::OK();
}
ta_[type].sample_count++;
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
if (!ta_[type].enabled) {
return Status::OK();
}
s = KeyStatsInsertion(type, column_family_id, key, value_size, ts);
if (!s.ok()) {
return Status::Corruption("Failed to insert key statistics");
}
return s;
}
// Before the analyzer is closed, the requested general statistic results are
// printed out here. In current stage, these information are not output to
// the files.
// -----type
// |__cf_id
// |_statistics
void TraceAnalyzer::PrintStatistics() {
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
ta_[type].total_keys = 0;
ta_[type].total_access = 0;
ta_[type].total_succ_access = 0;
printf("\n################# Operation Type: %s #####################\n",
ta_[type].type_name.c_str());
if (qps_ave_.size() == kTaTypeNum + 1) {
printf("Peak QPS is: %u Average QPS is: %f\n", qps_peak_[type],
qps_ave_[type]);
}
for (auto& stat_it : ta_[type].stats) {
if (stat_it.second.a_count == 0) {
continue;
}
TraceStats& stat = stat_it.second;
uint64_t total_a_keys = static_cast<uint64_t>(stat.a_key_stats.size());
double key_size_ave = 0.0;
double value_size_ave = 0.0;
double key_size_vari = 0.0;
double value_size_vari = 0.0;
if (stat.a_count > 0) {
key_size_ave =
(static_cast<double>(stat.a_key_size_sum)) / stat.a_count;
value_size_ave =
(static_cast<double>(stat.a_value_size_sum)) / stat.a_count;
key_size_vari = std::sqrt((static_cast<double>(stat.a_key_size_sqsum)) /
stat.a_count -
key_size_ave * key_size_ave);
value_size_vari = std::sqrt(
(static_cast<double>(stat.a_value_size_sqsum)) / stat.a_count -
value_size_ave * value_size_ave);
}
if (value_size_ave == 0.0) {
stat.a_value_mid = 0;
}
cfs_[stat.cf_id].a_count += total_a_keys;
ta_[type].total_keys += total_a_keys;
ta_[type].total_access += stat.a_count;
ta_[type].total_succ_access += stat.a_succ_count;
printf("*********************************************************\n");
printf("colume family id: %u\n", stat.cf_id);
printf("Total number of queries to this cf by %s: %" PRIu64 "\n",
ta_[type].type_name.c_str(), stat.a_count);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
printf("Total unique keys in this cf: %" PRIu64 "\n", total_a_keys);
printf("Average key size: %f key size medium: %" PRIu64
" Key size Variation: %f\n",
key_size_ave, stat.a_key_mid, key_size_vari);
if (type == kPut || type == kMerge) {
printf("Average value size: %f Value size medium: %" PRIu64
" Value size variation: %f\n",
value_size_ave, stat.a_value_mid, value_size_vari);
}
printf("Peak QPS is: %u Average QPS is: %f\n", stat.a_peak_qps,
stat.a_ave_qps);
// print the top k accessed key and its access count
if (FLAGS_print_top_k_access > 0) {
printf("The Top %d keys that are accessed:\n",
FLAGS_print_top_k_access);
while (!stat.top_k_queue.empty()) {
std::string hex_key =
rocksdb::LDBCommand::StringToHex(stat.top_k_queue.top().second);
printf("Access_count: %" PRIu64 " %s\n", stat.top_k_queue.top().first,
hex_key.c_str());
stat.top_k_queue.pop();
}
}
// print the top k access prefix range and
// top k prefix range with highest average access per key
if (FLAGS_output_prefix_cut > 0) {
printf("The Top %d accessed prefix range:\n", FLAGS_print_top_k_access);
while (!stat.top_k_prefix_access.empty()) {
printf("Prefix: %s Access count: %" PRIu64 "\n",
stat.top_k_prefix_access.top().second.c_str(),
stat.top_k_prefix_access.top().first);
stat.top_k_prefix_access.pop();
}
printf("The Top %d prefix with highest access per key:\n",
FLAGS_print_top_k_access);
while (!stat.top_k_prefix_ave.empty()) {
printf("Prefix: %s access per key: %f\n",
stat.top_k_prefix_ave.top().second.c_str(),
stat.top_k_prefix_ave.top().first);
stat.top_k_prefix_ave.pop();
}
}
// print the operation correlations
if (!FLAGS_print_correlation.empty()) {
for (int correlation = 0;
correlation <
static_cast<int>(analyzer_opts_.correlation_list.size());
correlation++) {
printf(
"The correlation statistics of '%s' after '%s' is:",
taIndexToOpt[analyzer_opts_.correlation_list[correlation].second]
.c_str(),
taIndexToOpt[analyzer_opts_.correlation_list[correlation].first]
.c_str());
double correlation_ave = 0.0;
if (stat.correlation_output[correlation].first > 0) {
correlation_ave =
(static_cast<double>(
stat.correlation_output[correlation].second)) /
(stat.correlation_output[correlation].first * 1000);
}
printf(" total numbers: %" PRIu64 " average time: %f(ms)\n",
stat.correlation_output[correlation].first, correlation_ave);
}
}
}
printf("*********************************************************\n");
printf("Total keys of '%s' is: %" PRIu64 "\n", ta_[type].type_name.c_str(),
ta_[type].total_keys);
printf("Total access is: %" PRIu64 "\n", ta_[type].total_access);
total_access_keys_ += ta_[type].total_keys;
}
// Print the overall statistic information of the trace
printf("\n*********************************************************\n");
printf("*********************************************************\n");
printf("The column family based statistics\n");
for (auto& cf : cfs_) {
printf("The column family id: %u\n", cf.first);
printf("The whole key space key numbers: %" PRIu64 "\n", cf.second.w_count);
printf("The accessed key space key numbers: %" PRIu64 "\n",
cf.second.a_count);
}
if (FLAGS_print_overall_stats) {
printf("\n*********************************************************\n");
printf("*********************************************************\n");
if (qps_peak_.size() == kTaTypeNum + 1) {
printf("Average QPS per second: %f Peak QPS: %u\n", qps_ave_[kTaTypeNum],
qps_peak_[kTaTypeNum]);
}
printf("The statistics related to query number need to times: %u\n",
sample_max_);
RocksDB Trace Analyzer (#4091) Summary: A framework of trace analyzing for RocksDB After collecting the trace by using the tool of [PR #3837](https://github.com/facebook/rocksdb/pull/3837). User can use the Trace Analyzer to interpret, analyze, and characterize the collected workload. **Input:** 1. trace file 2. Whole keys space file **Statistics:** 1. Access count of each operation (Get, Put, Delete, SingleDelete, DeleteRange, Merge) in each column family. 2. Key hotness (access count) of each one 3. Key space separation based on given prefix 4. Key size distribution 5. Value size distribution if appliable 6. Top K accessed keys 7. QPS statistics including the average QPS and peak QPS 8. Top K accessed prefix 9. The query correlation analyzing, output the number of X after Y and the corresponding average time intervals **Output:** 1. key access heat map (either in the accessed key space or whole key space) 2. trace sequence file (interpret the raw trace file to line base text file for future use) 3. Time serial (The key space ID and its access time) 4. Key access count distritbution 5. Key size distribution 6. Value size distribution (in each intervals) 7. whole key space separation by the prefix 8. Accessed key space separation by the prefix 9. QPS of each operation and each column family 10. Top K QPS and their accessed prefix range **Test:** 1. Added the unit test of analyzing Get, Put, Delete, SingleDelete, DeleteRange, Merge 2. Generated the trace and analyze the trace **Implemented but not tested (due to the limitation of trace_replay):** 1. Analyzing Iterator, supporting Seek() and SeekForPrev() analyzing 2. Analyzing the number of Key found by Get **Future Work:** 1. Support execution time analyzing of each requests 2. Support cache hit situation and block read situation of Get Pull Request resolved: https://github.com/facebook/rocksdb/pull/4091 Differential Revision: D9256157 Pulled By: zhichao-cao fbshipit-source-id: f0ceacb7eedbc43a3eee6e85b76087d7832a8fe6
2018-08-13 20:32:04 +02:00
printf("Total_requests: %" PRIu64 " Total_accessed_keys: %" PRIu64
" Total_gets: %" PRIu64 " Total_write_batch: %" PRIu64 "\n",
total_requests_, total_access_keys_, total_gets_, total_writes_);
for (int type = 0; type < kTaTypeNum; type++) {
if (!ta_[type].enabled) {
continue;
}
printf("Operation: '%s' has: %" PRIu64 "\n", ta_[type].type_name.c_str(),
ta_[type].total_access);
}
}
}
// Write the trace sequence to file
Status TraceAnalyzer::WriteTraceSequence(const uint32_t& type,
const uint32_t& cf_id,
const std::string& key,
const size_t value_size,
const uint64_t ts) {
std::string hex_key = rocksdb::LDBCommand::StringToHex(key);
int ret;
ret =
sprintf(buffer_, "%u %u %zu %" PRIu64 "\n", type, cf_id, value_size, ts);
if (ret < 0) {
return Status::IOError("failed to format the output");
}
std::string printout(buffer_);
if (!FLAGS_no_key) {
printout = hex_key + " " + printout;
}
return trace_sequence_f_->Append(printout);
}
// The entrance function of Trace_Analyzer
int trace_analyzer_tool(int argc, char** argv) {
std::string trace_path;
std::string output_path;
AnalyzerOptions analyzer_opts;
ParseCommandLineFlags(&argc, &argv, true);
if (!FLAGS_print_correlation.empty()) {
analyzer_opts.SparseCorrelationInput(FLAGS_print_correlation);
}
std::unique_ptr<TraceAnalyzer> analyzer(
new TraceAnalyzer(FLAGS_trace_path, FLAGS_output_dir, analyzer_opts));
if (!analyzer) {
fprintf(stderr, "Cannot initiate the trace analyzer\n");
exit(1);
}
rocksdb::Status s = analyzer->PrepareProcessing();
if (!s.ok()) {
fprintf(stderr, "%s\n", s.getState());
fprintf(stderr, "Cannot initiate the trace reader\n");
exit(1);
}
s = analyzer->StartProcessing();
if (!s.ok()) {
fprintf(stderr, "%s\n", s.getState());
fprintf(stderr, "Cannot processing the trace\n");
exit(1);
}
s = analyzer->MakeStatistics();
if (!s.ok()) {
fprintf(stderr, "%s\n", s.getState());
analyzer->EndProcessing();
fprintf(stderr, "Cannot make the statistics\n");
exit(1);
}
s = analyzer->ReProcessing();
if (!s.ok()) {
fprintf(stderr, "%s\n", s.getState());
fprintf(stderr, "Cannot re-process the trace for more statistics\n");
analyzer->EndProcessing();
exit(1);
}
s = analyzer->EndProcessing();
if (!s.ok()) {
fprintf(stderr, "%s\n", s.getState());
fprintf(stderr, "Cannot close the trace analyzer\n");
exit(1);
}
return 0;
}
} // namespace rocksdb
#endif // Endif of Gflag
#endif // RocksDB LITE