Block cache analysis and simulation tools

RocksDB configures a certain amount of main memory as a block cache to accelerate data access. Understanding the efficiency of block cache is very important. The block cache analysis and simulation tools help a user to collect block cache access traces, analyze its access pattern, and evaluate alternative caching policies.

Table of Contents

Quick Start
Tracing block cache accesses
Trace Format
Cache Simulations
Analyzing Block Cache Traces

Quick Start

db_bench supports tracing block cache accesses. This section demonstrates how to trace accesses when running db_bench. It also shows how to analyze and evaluate caching policies using the generated trace file.

Create a database:

./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --cache_index_and_filter_blocks --cache_size=1048576 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=10000000

To trace block cache accesses when running readrandom benchmark:

./db_bench --benchmarks="readrandom" --use_existing_db --duration=60 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --cache_index_and_filter_blocks --cache_size=1048576 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=10000000 --threads=16 -block_cache_trace_file="/tmp/trace" -block_cache_trace_max_trace_file_size_in_bytes=1073741824 -block_cache_trace_sampling_frequency=1

Convert the trace file to human readable format:

./block_cache_trace_analyzer -block_cache_trace_path=/tmp/block_trace_test_example -human_readable_trace_file_path=/tmp/human_readable_block_trace_test_example

Evaluate alternative caching policies:

bash block_cache_pysim.sh /tmp/human_readable_block_trace_test_example /tmp/sim_results/bench 1 0 30

Plot graphs:

python block_cache_trace_analyzer_plot.py /tmp/sim_results /tmp/sim_results_graphs

Tracing Block Cache Accesses

RocksDB supports block cache tracing APIs StartBlockCacheTrace and EndBlockCacheTrace. When tracing starts, RocksDB logs detailed information of block cache accesses into a trace file. A user must specify a trace option and trace file path when start tracing block cache accesses.

A trace option contains max_trace_file_size and sampling_frequency.

• max_trace_file_size specifies the maximum size of the trace file. The tracing stops when the trace file size exceeds the specified max_trace_file_size.
• sampling_frequency determines how frequent should RocksDB trace an access. RocksDB uses spatial downsampling such that it traces all accesses to sampled blocks. A sampling_frequency of 1 means tracing all block cache accesses. A sampling_frequency of 100 means tracing all accesses on ~1% blocks.

An example to start tracing block cache accesses:

Env* env = rocksdb::Env::Default();
EnvOptions env_options;
std::string trace_path = "/tmp/block_trace_test_example"
std::unique_ptr<TraceWriter> trace_writer;
DB* db = nullptr;
std::string db_name = "/tmp/rocksdb"

/*Create the trace file writer*/
NewFileTraceWriter(env, env_options, trace_path, &trace_writer);
DB::Open(options, dbname, &db);

/*Start tracing*/
db->StartBlockCacheTrace(trace_opt, std::move(trace_writer));

/* Your call of RocksDB APIs */

/*End tracing*/
db->EndBlockCacheTrace()

Trace file format

Cache Simulations

Supported simulators.

• RocksDB cache simulators.
• Python cache simulators.

Must first convert the binary trace file into human readable trace file.

Analyzing Block Cache Traces

Provides insights into how to improve a caching policy.