nathan/agrarian
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

r1

Browse Source
This commit is contained in:
pacificao
2022-02-03 23:45:47 -08:00
parent 42c2062cc4
commit 184ece190c
1438 changed files with 404064 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/leveldb/doc/bench/db_bench_sqlite3.cc
+718
View File
@@ -0,0 +1,718 @@
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#include <stdio.h>
#include <stdlib.h>
#include <sqlite3.h>
#include "util/histogram.h"
#include "util/random.h"
#include "util/testutil.h"
// Comma-separated list of operations to run in the specified order
// Actual benchmarks:
//
// fillseq -- write N values in sequential key order in async mode
// fillseqsync -- write N/100 values in sequential key order in sync mode
// fillseqbatch -- batch write N values in sequential key order in async mode
// fillrandom -- write N values in random key order in async mode
// fillrandsync -- write N/100 values in random key order in sync mode
// fillrandbatch -- batch write N values in sequential key order in async mode
// overwrite -- overwrite N values in random key order in async mode
// fillrand100K -- write N/1000 100K values in random order in async mode
// fillseq100K -- write N/1000 100K values in sequential order in async mode
// readseq -- read N times sequentially
// readrandom -- read N times in random order
// readrand100K -- read N/1000 100K values in sequential order in async mode
static const char* FLAGS_benchmarks =
"fillseq,"
"fillseqsync,"
"fillseqbatch,"
"fillrandom,"
"fillrandsync,"
"fillrandbatch,"
"overwrite,"
"overwritebatch,"
"readrandom,"
"readseq,"
"fillrand100K,"
"fillseq100K,"
"readseq,"
"readrand100K,"
;
// Number of key/values to place in database
static int FLAGS_num = 1000000;
// Number of read operations to do. If negative, do FLAGS_num reads.
static int FLAGS_reads = -1;
// Size of each value
static int FLAGS_value_size = 100;
// Print histogram of operation timings
static bool FLAGS_histogram = false;
// Arrange to generate values that shrink to this fraction of
// their original size after compression
static double FLAGS_compression_ratio = 0.5;
// Page size. Default 1 KB.
static int FLAGS_page_size = 1024;
// Number of pages.
// Default cache size = FLAGS_page_size * FLAGS_num_pages = 4 MB.
static int FLAGS_num_pages = 4096;
// If true, do not destroy the existing database. If you set this
// flag and also specify a benchmark that wants a fresh database, that
// benchmark will fail.
static bool FLAGS_use_existing_db = false;
// If true, we allow batch writes to occur
static bool FLAGS_transaction = true;
// If true, we enable Write-Ahead Logging
static bool FLAGS_WAL_enabled = true;
// Use the db with the following name.
static const char* FLAGS_db = NULL;
inline
static void ExecErrorCheck(int status, char *err_msg) {
if (status != SQLITE_OK) {
fprintf(stderr, "SQL error: %s\n", err_msg);
sqlite3_free(err_msg);
exit(1);
}
}
inline
static void StepErrorCheck(int status) {
if (status != SQLITE_DONE) {
fprintf(stderr, "SQL step error: status = %d\n", status);
exit(1);
}
}
inline
static void ErrorCheck(int status) {
if (status != SQLITE_OK) {
fprintf(stderr, "sqlite3 error: status = %d\n", status);
exit(1);
}
}
inline
static void WalCheckpoint(sqlite3* db_) {
// Flush all writes to disk
if (FLAGS_WAL_enabled) {
sqlite3_wal_checkpoint_v2(db_, NULL, SQLITE_CHECKPOINT_FULL, NULL, NULL);
}
}
namespace leveldb {
// Helper for quickly generating random data.
namespace {
class RandomGenerator {
private:
std::string data_;
int pos_;
public:
RandomGenerator() {
// We use a limited amount of data over and over again and ensure
// that it is larger than the compression window (32KB), and also
// large enough to serve all typical value sizes we want to write.
Random rnd(301);
std::string piece;
while (data_.size() < 1048576) {
// Add a short fragment that is as compressible as specified
// by FLAGS_compression_ratio.
test::CompressibleString(&rnd, FLAGS_compression_ratio, 100, &piece);
data_.append(piece);
}
pos_ = 0;
}
Slice Generate(int len) {
if (pos_ + len > data_.size()) {
pos_ = 0;
assert(len < data_.size());
}
pos_ += len;
return Slice(data_.data() + pos_ - len, len);
}
};
static Slice TrimSpace(Slice s) {
int start = 0;
while (start < s.size() && isspace(s[start])) {
start++;
}
int limit = s.size();
while (limit > start && isspace(s[limit-1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
} // namespace
class Benchmark {
private:
sqlite3* db_;
int db_num_;
int num_;
int reads_;
double start_;
double last_op_finish_;
int64_t bytes_;
std::string message_;
Histogram hist_;
RandomGenerator gen_;
Random rand_;
// State kept for progress messages
int done_;
int next_report_; // When to report next
void PrintHeader() {
const int kKeySize = 16;
PrintEnvironment();
fprintf(stdout, "Keys: %d bytes each\n", kKeySize);
fprintf(stdout, "Values: %d bytes each\n", FLAGS_value_size);
fprintf(stdout, "Entries: %d\n", num_);
fprintf(stdout, "RawSize: %.1f MB (estimated)\n",
((static_cast<int64_t>(kKeySize + FLAGS_value_size) * num_)
/ 1048576.0));
PrintWarnings();
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings() {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n"
);
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
}
void PrintEnvironment() {
fprintf(stderr, "SQLite: version %s\n", SQLITE_VERSION);
#if defined(__linux)
time_t now = time(NULL);
fprintf(stderr, "Date: %s", ctime(&now)); // ctime() adds newline
FILE* cpuinfo = fopen("/proc/cpuinfo", "r");
if (cpuinfo != NULL) {
char line[1000];
int num_cpus = 0;
std::string cpu_type;
std::string cache_size;
while (fgets(line, sizeof(line), cpuinfo) != NULL) {
const char* sep = strchr(line, ':');
if (sep == NULL) {
continue;
}
Slice key = TrimSpace(Slice(line, sep - 1 - line));
Slice val = TrimSpace(Slice(sep + 1));
if (key == "model name") {
++num_cpus;
cpu_type = val.ToString();
} else if (key == "cache size") {
cache_size = val.ToString();
}
}
fclose(cpuinfo);
fprintf(stderr, "CPU: %d * %s\n", num_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#endif
}
void Start() {
start_ = Env::Default()->NowMicros() * 1e-6;
bytes_ = 0;
message_.clear();
last_op_finish_ = start_;
hist_.Clear();
done_ = 0;
next_report_ = 100;
}
void FinishedSingleOp() {
if (FLAGS_histogram) {
double now = Env::Default()->NowMicros() * 1e-6;
double micros = (now - last_op_finish_) * 1e6;
hist_.Add(micros);
if (micros > 20000) {
fprintf(stderr, "long op: %.1f micros%30s\r", micros, "");
fflush(stderr);
}
last_op_finish_ = now;
}
done_++;
if (done_ >= next_report_) {
if (next_report_ < 1000) next_report_ += 100;
else if (next_report_ < 5000) next_report_ += 500;
else if (next_report_ < 10000) next_report_ += 1000;
else if (next_report_ < 50000) next_report_ += 5000;
else if (next_report_ < 100000) next_report_ += 10000;
else if (next_report_ < 500000) next_report_ += 50000;
else next_report_ += 100000;
fprintf(stderr, "... finished %d ops%30s\r", done_, "");
fflush(stderr);
}
}
void Stop(const Slice& name) {
double finish = Env::Default()->NowMicros() * 1e-6;
// Pretend at least one op was done in case we are running a benchmark
// that does not call FinishedSingleOp().
if (done_ < 1) done_ = 1;
if (bytes_ > 0) {
char rate[100];
snprintf(rate, sizeof(rate), "%6.1f MB/s",
(bytes_ / 1048576.0) / (finish - start_));
if (!message_.empty()) {
message_ = std::string(rate) + " " + message_;
} else {
message_ = rate;
}
}
fprintf(stdout, "%-12s : %11.3f micros/op;%s%s\n",
name.ToString().c_str(),
(finish - start_) * 1e6 / done_,
(message_.empty() ? "" : " "),
message_.c_str());
if (FLAGS_histogram) {
fprintf(stdout, "Microseconds per op:\n%s\n", hist_.ToString().c_str());
}
fflush(stdout);
}
public:
enum Order {
SEQUENTIAL,
RANDOM
};
enum DBState {
FRESH,
EXISTING
};
Benchmark()
: db_(NULL),
db_num_(0),
num_(FLAGS_num),
reads_(FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads),
bytes_(0),
rand_(301) {
std::vector<std::string> files;
std::string test_dir;
Env::Default()->GetTestDirectory(&test_dir);
Env::Default()->GetChildren(test_dir, &files);
if (!FLAGS_use_existing_db) {
for (int i = 0; i < files.size(); i++) {
if (Slice(files[i]).starts_with("dbbench_sqlite3")) {
std::string file_name(test_dir);
file_name += "/";
file_name += files[i];
Env::Default()->DeleteFile(file_name.c_str());
}
}
}
}
~Benchmark() {
int status = sqlite3_close(db_);
ErrorCheck(status);
}
void Run() {
PrintHeader();
Open();
const char* benchmarks = FLAGS_benchmarks;
while (benchmarks != NULL) {
const char* sep = strchr(benchmarks, ',');
Slice name;
if (sep == NULL) {
name = benchmarks;
benchmarks = NULL;
} else {
name = Slice(benchmarks, sep - benchmarks);
benchmarks = sep + 1;
}
bytes_ = 0;
Start();
bool known = true;
bool write_sync = false;
if (name == Slice("fillseq")) {
Write(write_sync, SEQUENTIAL, FRESH, num_, FLAGS_value_size, 1);
WalCheckpoint(db_);
} else if (name == Slice("fillseqbatch")) {
Write(write_sync, SEQUENTIAL, FRESH, num_, FLAGS_value_size, 1000);
WalCheckpoint(db_);
} else if (name == Slice("fillrandom")) {
Write(write_sync, RANDOM, FRESH, num_, FLAGS_value_size, 1);
WalCheckpoint(db_);
} else if (name == Slice("fillrandbatch")) {
Write(write_sync, RANDOM, FRESH, num_, FLAGS_value_size, 1000);
WalCheckpoint(db_);
} else if (name == Slice("overwrite")) {
Write(write_sync, RANDOM, EXISTING, num_, FLAGS_value_size, 1);
WalCheckpoint(db_);
} else if (name == Slice("overwritebatch")) {
Write(write_sync, RANDOM, EXISTING, num_, FLAGS_value_size, 1000);
WalCheckpoint(db_);
} else if (name == Slice("fillrandsync")) {
write_sync = true;
Write(write_sync, RANDOM, FRESH, num_ / 100, FLAGS_value_size, 1);
WalCheckpoint(db_);
} else if (name == Slice("fillseqsync")) {
write_sync = true;
Write(write_sync, SEQUENTIAL, FRESH, num_ / 100, FLAGS_value_size, 1);
WalCheckpoint(db_);
} else if (name == Slice("fillrand100K")) {
Write(write_sync, RANDOM, FRESH, num_ / 1000, 100 * 1000, 1);
WalCheckpoint(db_);
} else if (name == Slice("fillseq100K")) {
Write(write_sync, SEQUENTIAL, FRESH, num_ / 1000, 100 * 1000, 1);
WalCheckpoint(db_);
} else if (name == Slice("readseq")) {
ReadSequential();
} else if (name == Slice("readrandom")) {
Read(RANDOM, 1);
} else if (name == Slice("readrand100K")) {
int n = reads_;
reads_ /= 1000;
Read(RANDOM, 1);
reads_ = n;
} else {
known = false;
if (name != Slice()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.ToString().c_str());
}
}
if (known) {
Stop(name);
}
}
}
void Open() {
assert(db_ == NULL);
int status;
char file_name[100];
char* err_msg = NULL;
db_num_++;
// Open database
std::string tmp_dir;
Env::Default()->GetTestDirectory(&tmp_dir);
snprintf(file_name, sizeof(file_name),
"%s/dbbench_sqlite3-%d.db",
tmp_dir.c_str(),
db_num_);
status = sqlite3_open(file_name, &db_);
if (status) {
fprintf(stderr, "open error: %s\n", sqlite3_errmsg(db_));
exit(1);
}
// Change SQLite cache size
char cache_size[100];
snprintf(cache_size, sizeof(cache_size), "PRAGMA cache_size = %d",
FLAGS_num_pages);
status = sqlite3_exec(db_, cache_size, NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
// FLAGS_page_size is defaulted to 1024
if (FLAGS_page_size != 1024) {
char page_size[100];
snprintf(page_size, sizeof(page_size), "PRAGMA page_size = %d",
FLAGS_page_size);
status = sqlite3_exec(db_, page_size, NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
}
// Change journal mode to WAL if WAL enabled flag is on
if (FLAGS_WAL_enabled) {
std::string WAL_stmt = "PRAGMA journal_mode = WAL";
// LevelDB's default cache size is a combined 4 MB
std::string WAL_checkpoint = "PRAGMA wal_autocheckpoint = 4096";
status = sqlite3_exec(db_, WAL_stmt.c_str(), NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
status = sqlite3_exec(db_, WAL_checkpoint.c_str(), NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
}
// Change locking mode to exclusive and create tables/index for database
std::string locking_stmt = "PRAGMA locking_mode = EXCLUSIVE";
std::string create_stmt =
"CREATE TABLE test (key blob, value blob, PRIMARY KEY(key))";
std::string stmt_array[] = { locking_stmt, create_stmt };
int stmt_array_length = sizeof(stmt_array) / sizeof(std::string);
for (int i = 0; i < stmt_array_length; i++) {
status = sqlite3_exec(db_, stmt_array[i].c_str(), NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
}
}
void Write(bool write_sync, Order order, DBState state,
int num_entries, int value_size, int entries_per_batch) {
// Create new database if state == FRESH
if (state == FRESH) {
if (FLAGS_use_existing_db) {
message_ = "skipping (--use_existing_db is true)";
return;
}
sqlite3_close(db_);
db_ = NULL;
Open();
Start();
}
if (num_entries != num_) {
char msg[100];
snprintf(msg, sizeof(msg), "(%d ops)", num_entries);
message_ = msg;
}
char* err_msg = NULL;
int status;
sqlite3_stmt *replace_stmt, *begin_trans_stmt, *end_trans_stmt;
std::string replace_str = "REPLACE INTO test (key, value) VALUES (?, ?)";
std::string begin_trans_str = "BEGIN TRANSACTION;";
std::string end_trans_str = "END TRANSACTION;";
// Check for synchronous flag in options
std::string sync_stmt = (write_sync) ? "PRAGMA synchronous = FULL" :
"PRAGMA synchronous = OFF";
status = sqlite3_exec(db_, sync_stmt.c_str(), NULL, NULL, &err_msg);
ExecErrorCheck(status, err_msg);
// Preparing sqlite3 statements
status = sqlite3_prepare_v2(db_, replace_str.c_str(), -1,
&replace_stmt, NULL);
ErrorCheck(status);
status = sqlite3_prepare_v2(db_, begin_trans_str.c_str(), -1,
&begin_trans_stmt, NULL);
ErrorCheck(status);
status = sqlite3_prepare_v2(db_, end_trans_str.c_str(), -1,
&end_trans_stmt, NULL);
ErrorCheck(status);
bool transaction = (entries_per_batch > 1);
for (int i = 0; i < num_entries; i += entries_per_batch) {
// Begin write transaction
if (FLAGS_transaction && transaction) {
status = sqlite3_step(begin_trans_stmt);
StepErrorCheck(status);
status = sqlite3_reset(begin_trans_stmt);
ErrorCheck(status);
}
// Create and execute SQL statements
for (int j = 0; j < entries_per_batch; j++) {
const char* value = gen_.Generate(value_size).data();
// Create values for key-value pair
const int k = (order == SEQUENTIAL) ? i + j :
(rand_.Next() % num_entries);
char key[100];
snprintf(key, sizeof(key), "%016d", k);
// Bind KV values into replace_stmt
status = sqlite3_bind_blob(replace_stmt, 1, key, 16, SQLITE_STATIC);
ErrorCheck(status);
status = sqlite3_bind_blob(replace_stmt, 2, value,
value_size, SQLITE_STATIC);
ErrorCheck(status);
// Execute replace_stmt
bytes_ += value_size + strlen(key);
status = sqlite3_step(replace_stmt);
StepErrorCheck(status);
// Reset SQLite statement for another use
status = sqlite3_clear_bindings(replace_stmt);
ErrorCheck(status);
status = sqlite3_reset(replace_stmt);
ErrorCheck(status);
FinishedSingleOp();
}
// End write transaction
if (FLAGS_transaction && transaction) {
status = sqlite3_step(end_trans_stmt);
StepErrorCheck(status);
status = sqlite3_reset(end_trans_stmt);
ErrorCheck(status);
}
}
status = sqlite3_finalize(replace_stmt);
ErrorCheck(status);
status = sqlite3_finalize(begin_trans_stmt);
ErrorCheck(status);
status = sqlite3_finalize(end_trans_stmt);
ErrorCheck(status);
}
void Read(Order order, int entries_per_batch) {
int status;
sqlite3_stmt *read_stmt, *begin_trans_stmt, *end_trans_stmt;
std::string read_str = "SELECT * FROM test WHERE key = ?";
std::string begin_trans_str = "BEGIN TRANSACTION;";
std::string end_trans_str = "END TRANSACTION;";
// Preparing sqlite3 statements
status = sqlite3_prepare_v2(db_, begin_trans_str.c_str(), -1,
&begin_trans_stmt, NULL);
ErrorCheck(status);
status = sqlite3_prepare_v2(db_, end_trans_str.c_str(), -1,
&end_trans_stmt, NULL);
ErrorCheck(status);
status = sqlite3_prepare_v2(db_, read_str.c_str(), -1, &read_stmt, NULL);
ErrorCheck(status);
bool transaction = (entries_per_batch > 1);
for (int i = 0; i < reads_; i += entries_per_batch) {
// Begin read transaction
if (FLAGS_transaction && transaction) {
status = sqlite3_step(begin_trans_stmt);
StepErrorCheck(status);
status = sqlite3_reset(begin_trans_stmt);
ErrorCheck(status);
}
// Create and execute SQL statements
for (int j = 0; j < entries_per_batch; j++) {
// Create key value
char key[100];
int k = (order == SEQUENTIAL) ? i + j : (rand_.Next() % reads_);
snprintf(key, sizeof(key), "%016d", k);
// Bind key value into read_stmt
status = sqlite3_bind_blob(read_stmt, 1, key, 16, SQLITE_STATIC);
ErrorCheck(status);
// Execute read statement
while ((status = sqlite3_step(read_stmt)) == SQLITE_ROW) {}
StepErrorCheck(status);
// Reset SQLite statement for another use
status = sqlite3_clear_bindings(read_stmt);
ErrorCheck(status);
status = sqlite3_reset(read_stmt);
ErrorCheck(status);
FinishedSingleOp();
}
// End read transaction
if (FLAGS_transaction && transaction) {
status = sqlite3_step(end_trans_stmt);
StepErrorCheck(status);
status = sqlite3_reset(end_trans_stmt);
ErrorCheck(status);
}
}
status = sqlite3_finalize(read_stmt);
ErrorCheck(status);
status = sqlite3_finalize(begin_trans_stmt);
ErrorCheck(status);
status = sqlite3_finalize(end_trans_stmt);
ErrorCheck(status);
}
void ReadSequential() {
int status;
sqlite3_stmt *pStmt;
std::string read_str = "SELECT * FROM test ORDER BY key";
status = sqlite3_prepare_v2(db_, read_str.c_str(), -1, &pStmt, NULL);
ErrorCheck(status);
for (int i = 0; i < reads_ && SQLITE_ROW == sqlite3_step(pStmt); i++) {
bytes_ += sqlite3_column_bytes(pStmt, 1) + sqlite3_column_bytes(pStmt, 2);
FinishedSingleOp();
}
status = sqlite3_finalize(pStmt);
ErrorCheck(status);
}
};
} // namespace leveldb
int main(int argc, char** argv) {
std::string default_db_path;
for (int i = 1; i < argc; i++) {
double d;
int n;
char junk;
if (leveldb::Slice(argv[i]).starts_with("--benchmarks=")) {
FLAGS_benchmarks = argv[i] + strlen("--benchmarks=");
} else if (sscanf(argv[i], "--histogram=%d%c", &n, &junk) == 1 &&
(n == 0 || n == 1)) {
FLAGS_histogram = n;
} else if (sscanf(argv[i], "--compression_ratio=%lf%c", &d, &junk) == 1) {
FLAGS_compression_ratio = d;
} else if (sscanf(argv[i], "--use_existing_db=%d%c", &n, &junk) == 1 &&
(n == 0 || n == 1)) {
FLAGS_use_existing_db = n;
} else if (sscanf(argv[i], "--num=%d%c", &n, &junk) == 1) {
FLAGS_num = n;
} else if (sscanf(argv[i], "--reads=%d%c", &n, &junk) == 1) {
FLAGS_reads = n;
} else if (sscanf(argv[i], "--value_size=%d%c", &n, &junk) == 1) {
FLAGS_value_size = n;
} else if (leveldb::Slice(argv[i]) == leveldb::Slice("--no_transaction")) {
FLAGS_transaction = false;
} else if (sscanf(argv[i], "--page_size=%d%c", &n, &junk) == 1) {
FLAGS_page_size = n;
} else if (sscanf(argv[i], "--num_pages=%d%c", &n, &junk) == 1) {
FLAGS_num_pages = n;
} else if (sscanf(argv[i], "--WAL_enabled=%d%c", &n, &junk) == 1 &&
(n == 0 || n == 1)) {
FLAGS_WAL_enabled = n;
} else if (strncmp(argv[i], "--db=", 5) == 0) {
FLAGS_db = argv[i] + 5;
} else {
fprintf(stderr, "Invalid flag '%s'\n", argv[i]);
exit(1);
}
}
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db == NULL) {
leveldb::Env::Default()->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path.c_str();
}
leveldb::Benchmark benchmark;
benchmark.Run();
return 0;
}
src/leveldb/doc/bench/db_bench_tree_db.cc
+528
View File
@@ -0,0 +1,528 @@
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#include <stdio.h>
#include <stdlib.h>
#include <kcpolydb.h>
#include "util/histogram.h"
#include "util/random.h"
#include "util/testutil.h"
// Comma-separated list of operations to run in the specified order
// Actual benchmarks:
//
// fillseq -- write N values in sequential key order in async mode
// fillrandom -- write N values in random key order in async mode
// overwrite -- overwrite N values in random key order in async mode
// fillseqsync -- write N/100 values in sequential key order in sync mode
// fillrandsync -- write N/100 values in random key order in sync mode
// fillrand100K -- write N/1000 100K values in random order in async mode
// fillseq100K -- write N/1000 100K values in seq order in async mode
// readseq -- read N times sequentially
// readseq100K -- read N/1000 100K values in sequential order in async mode
// readrand100K -- read N/1000 100K values in sequential order in async mode
// readrandom -- read N times in random order
static const char* FLAGS_benchmarks =
"fillseq,"
"fillseqsync,"
"fillrandsync,"
"fillrandom,"
"overwrite,"
"readrandom,"
"readseq,"
"fillrand100K,"
"fillseq100K,"
"readseq100K,"
"readrand100K,"
;
// Number of key/values to place in database
static int FLAGS_num = 1000000;
// Number of read operations to do. If negative, do FLAGS_num reads.
static int FLAGS_reads = -1;
// Size of each value
static int FLAGS_value_size = 100;
// Arrange to generate values that shrink to this fraction of
// their original size after compression
static double FLAGS_compression_ratio = 0.5;
// Print histogram of operation timings
static bool FLAGS_histogram = false;
// Cache size. Default 4 MB
static int FLAGS_cache_size = 4194304;
// Page size. Default 1 KB
static int FLAGS_page_size = 1024;
// If true, do not destroy the existing database. If you set this
// flag and also specify a benchmark that wants a fresh database, that
// benchmark will fail.
static bool FLAGS_use_existing_db = false;
// Compression flag. If true, compression is on. If false, compression
// is off.
static bool FLAGS_compression = true;
// Use the db with the following name.
static const char* FLAGS_db = NULL;
inline
static void DBSynchronize(kyotocabinet::TreeDB* db_)
{
// Synchronize will flush writes to disk
if (!db_->synchronize()) {
fprintf(stderr, "synchronize error: %s\n", db_->error().name());
}
}
namespace leveldb {
// Helper for quickly generating random data.
namespace {
class RandomGenerator {
private:
std::string data_;
int pos_;
public:
RandomGenerator() {
// We use a limited amount of data over and over again and ensure
// that it is larger than the compression window (32KB), and also
// large enough to serve all typical value sizes we want to write.
Random rnd(301);
std::string piece;
while (data_.size() < 1048576) {
// Add a short fragment that is as compressible as specified
// by FLAGS_compression_ratio.
test::CompressibleString(&rnd, FLAGS_compression_ratio, 100, &piece);
data_.append(piece);
}
pos_ = 0;
}
Slice Generate(int len) {
if (pos_ + len > data_.size()) {
pos_ = 0;
assert(len < data_.size());
}
pos_ += len;
return Slice(data_.data() + pos_ - len, len);
}
};
static Slice TrimSpace(Slice s) {
int start = 0;
while (start < s.size() && isspace(s[start])) {
start++;
}
int limit = s.size();
while (limit > start && isspace(s[limit-1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
} // namespace
class Benchmark {
private:
kyotocabinet::TreeDB* db_;
int db_num_;
int num_;
int reads_;
double start_;
double last_op_finish_;
int64_t bytes_;
std::string message_;
Histogram hist_;
RandomGenerator gen_;
Random rand_;
kyotocabinet::LZOCompressor<kyotocabinet::LZO::RAW> comp_;
// State kept for progress messages
int done_;
int next_report_; // When to report next
void PrintHeader() {
const int kKeySize = 16;
PrintEnvironment();
fprintf(stdout, "Keys: %d bytes each\n", kKeySize);
fprintf(stdout, "Values: %d bytes each (%d bytes after compression)\n",
FLAGS_value_size,
static_cast<int>(FLAGS_value_size * FLAGS_compression_ratio + 0.5));
fprintf(stdout, "Entries: %d\n", num_);
fprintf(stdout, "RawSize: %.1f MB (estimated)\n",
((static_cast<int64_t>(kKeySize + FLAGS_value_size) * num_)
/ 1048576.0));
fprintf(stdout, "FileSize: %.1f MB (estimated)\n",
(((kKeySize + FLAGS_value_size * FLAGS_compression_ratio) * num_)
/ 1048576.0));
PrintWarnings();
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings() {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n"
);
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
}
void PrintEnvironment() {
fprintf(stderr, "Kyoto Cabinet: version %s, lib ver %d, lib rev %d\n",
kyotocabinet::VERSION, kyotocabinet::LIBVER, kyotocabinet::LIBREV);
#if defined(__linux)
time_t now = time(NULL);
fprintf(stderr, "Date: %s", ctime(&now)); // ctime() adds newline
FILE* cpuinfo = fopen("/proc/cpuinfo", "r");
if (cpuinfo != NULL) {
char line[1000];
int num_cpus = 0;
std::string cpu_type;
std::string cache_size;
while (fgets(line, sizeof(line), cpuinfo) != NULL) {
const char* sep = strchr(line, ':');
if (sep == NULL) {
continue;
}
Slice key = TrimSpace(Slice(line, sep - 1 - line));
Slice val = TrimSpace(Slice(sep + 1));
if (key == "model name") {
++num_cpus;
cpu_type = val.ToString();
} else if (key == "cache size") {
cache_size = val.ToString();
}
}
fclose(cpuinfo);
fprintf(stderr, "CPU: %d * %s\n", num_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#endif
}
void Start() {
start_ = Env::Default()->NowMicros() * 1e-6;
bytes_ = 0;
message_.clear();
last_op_finish_ = start_;
hist_.Clear();
done_ = 0;
next_report_ = 100;
}
void FinishedSingleOp() {
if (FLAGS_histogram) {
double now = Env::Default()->NowMicros() * 1e-6;
double micros = (now - last_op_finish_) * 1e6;
hist_.Add(micros);
if (micros > 20000) {
fprintf(stderr, "long op: %.1f micros%30s\r", micros, "");
fflush(stderr);
}
last_op_finish_ = now;
}
done_++;
if (done_ >= next_report_) {
if (next_report_ < 1000) next_report_ += 100;
else if (next_report_ < 5000) next_report_ += 500;
else if (next_report_ < 10000) next_report_ += 1000;
else if (next_report_ < 50000) next_report_ += 5000;
else if (next_report_ < 100000) next_report_ += 10000;
else if (next_report_ < 500000) next_report_ += 50000;
else next_report_ += 100000;
fprintf(stderr, "... finished %d ops%30s\r", done_, "");
fflush(stderr);
}
}
void Stop(const Slice& name) {
double finish = Env::Default()->NowMicros() * 1e-6;
// Pretend at least one op was done in case we are running a benchmark
// that does not call FinishedSingleOp().
if (done_ < 1) done_ = 1;
if (bytes_ > 0) {
char rate[100];
snprintf(rate, sizeof(rate), "%6.1f MB/s",
(bytes_ / 1048576.0) / (finish - start_));
if (!message_.empty()) {
message_ = std::string(rate) + " " + message_;
} else {
message_ = rate;
}
}
fprintf(stdout, "%-12s : %11.3f micros/op;%s%s\n",
name.ToString().c_str(),
(finish - start_) * 1e6 / done_,
(message_.empty() ? "" : " "),
message_.c_str());
if (FLAGS_histogram) {
fprintf(stdout, "Microseconds per op:\n%s\n", hist_.ToString().c_str());
}
fflush(stdout);
}
public:
enum Order {
SEQUENTIAL,
RANDOM
};
enum DBState {
FRESH,
EXISTING
};
Benchmark()
: db_(NULL),
num_(FLAGS_num),
reads_(FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads),
bytes_(0),
rand_(301) {
std::vector<std::string> files;
std::string test_dir;
Env::Default()->GetTestDirectory(&test_dir);
Env::Default()->GetChildren(test_dir.c_str(), &files);
if (!FLAGS_use_existing_db) {
for (int i = 0; i < files.size(); i++) {
if (Slice(files[i]).starts_with("dbbench_polyDB")) {
std::string file_name(test_dir);
file_name += "/";
file_name += files[i];
Env::Default()->DeleteFile(file_name.c_str());
}
}
}
}
~Benchmark() {
if (!db_->close()) {
fprintf(stderr, "close error: %s\n", db_->error().name());
}
}
void Run() {
PrintHeader();
Open(false);
const char* benchmarks = FLAGS_benchmarks;
while (benchmarks != NULL) {
const char* sep = strchr(benchmarks, ',');
Slice name;
if (sep == NULL) {
name = benchmarks;
benchmarks = NULL;
} else {
name = Slice(benchmarks, sep - benchmarks);
benchmarks = sep + 1;
}
Start();
bool known = true;
bool write_sync = false;
if (name == Slice("fillseq")) {
Write(write_sync, SEQUENTIAL, FRESH, num_, FLAGS_value_size, 1);
DBSynchronize(db_);
} else if (name == Slice("fillrandom")) {
Write(write_sync, RANDOM, FRESH, num_, FLAGS_value_size, 1);
DBSynchronize(db_);
} else if (name == Slice("overwrite")) {
Write(write_sync, RANDOM, EXISTING, num_, FLAGS_value_size, 1);
DBSynchronize(db_);
} else if (name == Slice("fillrandsync")) {
write_sync = true;
Write(write_sync, RANDOM, FRESH, num_ / 100, FLAGS_value_size, 1);
DBSynchronize(db_);
} else if (name == Slice("fillseqsync")) {
write_sync = true;
Write(write_sync, SEQUENTIAL, FRESH, num_ / 100, FLAGS_value_size, 1);
DBSynchronize(db_);
} else if (name == Slice("fillrand100K")) {
Write(write_sync, RANDOM, FRESH, num_ / 1000, 100 * 1000, 1);
DBSynchronize(db_);
} else if (name == Slice("fillseq100K")) {
Write(write_sync, SEQUENTIAL, FRESH, num_ / 1000, 100 * 1000, 1);
DBSynchronize(db_);
} else if (name == Slice("readseq")) {
ReadSequential();
} else if (name == Slice("readrandom")) {
ReadRandom();
} else if (name == Slice("readrand100K")) {
int n = reads_;
reads_ /= 1000;
ReadRandom();
reads_ = n;
} else if (name == Slice("readseq100K")) {
int n = reads_;
reads_ /= 1000;
ReadSequential();
reads_ = n;
} else {
known = false;
if (name != Slice()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.ToString().c_str());
}
}
if (known) {
Stop(name);
}
}
}
private:
void Open(bool sync) {
assert(db_ == NULL);
// Initialize db_
db_ = new kyotocabinet::TreeDB();
char file_name[100];
db_num_++;
std::string test_dir;
Env::Default()->GetTestDirectory(&test_dir);
snprintf(file_name, sizeof(file_name),
"%s/dbbench_polyDB-%d.kct",
test_dir.c_str(),
db_num_);
// Create tuning options and open the database
int open_options = kyotocabinet::PolyDB::OWRITER |
kyotocabinet::PolyDB::OCREATE;
int tune_options = kyotocabinet::TreeDB::TSMALL |
kyotocabinet::TreeDB::TLINEAR;
if (FLAGS_compression) {
tune_options |= kyotocabinet::TreeDB::TCOMPRESS;
db_->tune_compressor(&comp_);
}
db_->tune_options(tune_options);
db_->tune_page_cache(FLAGS_cache_size);
db_->tune_page(FLAGS_page_size);
db_->tune_map(256LL<<20);
if (sync) {
open_options |= kyotocabinet::PolyDB::OAUTOSYNC;
}
if (!db_->open(file_name, open_options)) {
fprintf(stderr, "open error: %s\n", db_->error().name());
}
}
void Write(bool sync, Order order, DBState state,
int num_entries, int value_size, int entries_per_batch) {
// Create new database if state == FRESH
if (state == FRESH) {
if (FLAGS_use_existing_db) {
message_ = "skipping (--use_existing_db is true)";
return;
}
delete db_;
db_ = NULL;
Open(sync);
Start(); // Do not count time taken to destroy/open
}
if (num_entries != num_) {
char msg[100];
snprintf(msg, sizeof(msg), "(%d ops)", num_entries);
message_ = msg;
}
// Write to database
for (int i = 0; i < num_entries; i++)
{
const int k = (order == SEQUENTIAL) ? i : (rand_.Next() % num_entries);
char key[100];
snprintf(key, sizeof(key), "%016d", k);
bytes_ += value_size + strlen(key);
std::string cpp_key = key;
if (!db_->set(cpp_key, gen_.Generate(value_size).ToString())) {
fprintf(stderr, "set error: %s\n", db_->error().name());
}
FinishedSingleOp();
}
}
void ReadSequential() {
kyotocabinet::DB::Cursor* cur = db_->cursor();
cur->jump();
std::string ckey, cvalue;
while (cur->get(&ckey, &cvalue, true)) {
bytes_ += ckey.size() + cvalue.size();
FinishedSingleOp();
}
delete cur;
}
void ReadRandom() {
std::string value;
for (int i = 0; i < reads_; i++) {
char key[100];
const int k = rand_.Next() % reads_;
snprintf(key, sizeof(key), "%016d", k);
db_->get(key, &value);
FinishedSingleOp();
}
}
};
} // namespace leveldb
int main(int argc, char** argv) {
std::string default_db_path;
for (int i = 1; i < argc; i++) {
double d;
int n;
char junk;
if (leveldb::Slice(argv[i]).starts_with("--benchmarks=")) {
FLAGS_benchmarks = argv[i] + strlen("--benchmarks=");
} else if (sscanf(argv[i], "--compression_ratio=%lf%c", &d, &junk) == 1) {
FLAGS_compression_ratio = d;
} else if (sscanf(argv[i], "--histogram=%d%c", &n, &junk) == 1 &&
(n == 0 || n == 1)) {
FLAGS_histogram = n;
} else if (sscanf(argv[i], "--num=%d%c", &n, &junk) == 1) {
FLAGS_num = n;
} else if (sscanf(argv[i], "--reads=%d%c", &n, &junk) == 1) {
FLAGS_reads = n;
} else if (sscanf(argv[i], "--value_size=%d%c", &n, &junk) == 1) {
FLAGS_value_size = n;
} else if (sscanf(argv[i], "--cache_size=%d%c", &n, &junk) == 1) {
FLAGS_cache_size = n;
} else if (sscanf(argv[i], "--page_size=%d%c", &n, &junk) == 1) {
FLAGS_page_size = n;
} else if (sscanf(argv[i], "--compression=%d%c", &n, &junk) == 1 &&
(n == 0 || n == 1)) {
FLAGS_compression = (n == 1) ? true : false;
} else if (strncmp(argv[i], "--db=", 5) == 0) {
FLAGS_db = argv[i] + 5;
} else {
fprintf(stderr, "Invalid flag '%s'\n", argv[i]);
exit(1);
}
}
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db == NULL) {
leveldb::Env::Default()->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path.c_str();
}
leveldb::Benchmark benchmark;
benchmark.Run();
return 0;
}
src/leveldb/doc/benchmark.html
+459
View File
@@ -0,0 +1,459 @@
<!DOCTYPE html>
<html>
<head>
<title>LevelDB Benchmarks</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<style>
body {
font-family:Helvetica,sans-serif;
padding:20px;
}
h2 {
padding-top:30px;
}
table.bn {
width:800px;
border-collapse:collapse;
border:0;
padding:0;
}
table.bnbase {
width:650px;
}
table.bn td {
padding:2px 0;
}
table.bn td.c1 {
font-weight:bold;
width:150px;
}
table.bn td.c1 div.e {
float:right;
font-weight:normal;
}
table.bn td.c2 {
width:150px;
text-align:right;
padding:2px;
}
table.bn td.c3 {
width:350px;
}
table.bn td.c4 {
width:150px;
font-size:small;
padding-left:4px;
}
/* chart bars */
div.bldb {
background-color:#0255df;
}
div.bkct {
background-color:#df5555;
}
div.bsql {
background-color:#aadf55;
}
.code {
font-family:monospace;
font-size:large;
}
.todo {
color: red;
}
</style>
</head>
<body>
<h1>LevelDB Benchmarks</h1>
<p>Google, July 2011</p>
<hr>
<p>In order to test LevelDB's performance, we benchmark it against other well-established database implementations. We compare LevelDB (revision 39) against <a href="http://www.sqlite.org/">SQLite3</a> (version 3.7.6.3) and <a href="http://fallabs.com/kyotocabinet/spex.html">Kyoto Cabinet's</a> (version 1.2.67) TreeDB (a B+Tree based key-value store). We would like to acknowledge Scott Hess and Mikio Hirabayashi for their suggestions and contributions to the SQLite3 and Kyoto Cabinet benchmarks, respectively.</p>
<p>Benchmarks were all performed on a six-core Intel(R) Xeon(R) CPU X5650 @ 2.67GHz, with 12288 KB of total L3 cache and 12 GB of DDR3 RAM at 1333 MHz. (Note that LevelDB uses at most two CPUs since the benchmarks are single threaded: one to run the benchmark, and one for background compactions.) We ran the benchmarks on two machines (with identical processors), one with an Ext3 file system and one with an Ext4 file system. The machine with the Ext3 file system has a SATA Hitachi HDS721050CLA362 hard drive. The machine with the Ext4 file system has a SATA Samsung HD502HJ hard drive. Both hard drives spin at 7200 RPM and have hard drive write-caching enabled (using `hdparm -W 1 [device]`). The numbers reported below are the median of three measurements.</p>
<h4>Benchmark Source Code</h4>
<p>We wrote benchmark tools for SQLite and Kyoto TreeDB based on LevelDB's <span class="code">db_bench</span>. The code for each of the benchmarks resides here:</p>
<ul>
<li> <b>LevelDB:</b> <a href="http://code.google.com/p/leveldb/source/browse/trunk/db/db_bench.cc">db/db_bench.cc</a>.</li>
<li> <b>SQLite:</b> <a href="http://code.google.com/p/leveldb/source/browse/#svn%2Ftrunk%2Fdoc%2Fbench%2Fdb_bench_sqlite3.cc">doc/bench/db_bench_sqlite3.cc</a>.</li>
<li> <b>Kyoto TreeDB:</b> <a href="http://code.google.com/p/leveldb/source/browse/#svn%2Ftrunk%2Fdoc%2Fbench%2Fdb_bench_tree_db.cc">doc/bench/db_bench_tree_db.cc</a>.</li>
</ul>
<h4>Custom Build Specifications</h4>
<ul>
<li>LevelDB: LevelDB was compiled with the <a href="http://code.google.com/p/google-perftools">tcmalloc</a> library and the <a href="http://code.google.com/p/snappy/">Snappy</a> compression library (revision 33). Assertions were disabled.</li>
<li>TreeDB: TreeDB was compiled using the <a href="http://www.oberhumer.com/opensource/lzo/">LZO</a> compression library (version 2.03). Furthermore, we enabled the TSMALL and TLINEAR options when opening the database in order to reduce the footprint of each record.</li>
<li>SQLite: We tuned SQLite's performance, by setting its locking mode to exclusive. We also enabled SQLite's <a href="http://www.sqlite.org/draft/wal.html">write-ahead logging</a>.</li>
</ul>
<h2>1. Baseline Performance</h2>
<p>This section gives the baseline performance of all the
databases. Following sections show how performance changes as various
parameters are varied. For the baseline:</p>
<ul>
<li> Each database is allowed 4 MB of cache memory.</li>
<li> Databases are opened in <em>asynchronous</em> write mode.
(LevelDB's sync option, TreeDB's OAUTOSYNC option, and
SQLite3's synchronous options are all turned off). I.e.,
every write is pushed to the operating system, but the
benchmark does not wait for the write to reach the disk.</li>
<li> Keys are 16 bytes each.</li>
<li> Value are 100 bytes each (with enough redundancy so that
a simple compressor shrinks them to 50% of their original
size).</li>
<li> Sequential reads/writes traverse the key space in increasing order.</li>
<li> Random reads/writes traverse the key space in random order.</li>
</ul>
<h3>A. Sequential Reads</h3>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">4,030,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">1,010,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:95px">&nbsp;</div></td>
<tr><td class="c1">SQLite3</td>
<td class="c2">383,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:33px">&nbsp;</div></td>
</table>
<h3>B. Random Reads</h3>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">129,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:298px">&nbsp;</div></td>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">151,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:350px">&nbsp;</div></td>
<tr><td class="c1">SQLite3</td>
<td class="c2">134,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:310px">&nbsp;</div></td>
</table>
<h3>C. Sequential Writes</h3>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">779,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">342,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:154px">&nbsp;</div></td>
<tr><td class="c1">SQLite3</td>
<td class="c2">48,600 ops/sec</td>
<td class="c3"><div class="bsql" style="width:22px">&nbsp;</div></td>
</table>
<h3>D. Random Writes</h3>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">164,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">88,500 ops/sec</td>
<td class="c3"><div class="bkct" style="width:188px">&nbsp;</div></td>
<tr><td class="c1">SQLite3</td>
<td class="c2">9,860 ops/sec</td>
<td class="c3"><div class="bsql" style="width:21px">&nbsp;</div></td>
</table>
<p>LevelDB outperforms both SQLite3 and TreeDB in sequential and random write operations and sequential read operations. Kyoto Cabinet has the fastest random read operations.</p>
<h2>2. Write Performance under Different Configurations</h2>
<h3>A. Large Values </h3>
<p>For this benchmark, we start with an empty database, and write 100,000 byte values (~50% compressible). To keep the benchmark running time reasonable, we stop after writing 1000 values.</p>
<h4>Sequential Writes</h4>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">1,100 ops/sec</td>
<td class="c3"><div class="bldb" style="width:234px">&nbsp;</div></td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">1,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:224px">&nbsp;</div></td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">1,600 ops/sec</td>
<td class="c3"><div class="bsql" style="width:350px">&nbsp;</div></td></tr>
</table>
<h4>Random Writes</h4>
<table class="bn bnbase">
<tr><td class="c1">LevelDB</td>
<td class="c2">480 ops/sec</td>
<td class="c3"><div class="bldb" style="width:105px">&nbsp;</div></td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">1,100 ops/sec</td>
<td class="c3"><div class="bkct" style="width:240px">&nbsp;</div></td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">1,600 ops/sec</td>
<td class="c3"><div class="bsql" style="width:350px">&nbsp;</div></td></tr>
</table>
<p>LevelDB doesn't perform as well with large values of 100,000 bytes each. This is because LevelDB writes keys and values at least twice: first time to the transaction log, and second time (during a compaction) to a sorted file.
With larger values, LevelDB's per-operation efficiency is swamped by the
cost of extra copies of large values.</p>
<h3>B. Batch Writes</h3>
<p>A batch write is a set of writes that are applied atomically to the underlying database. A single batch of N writes may be significantly faster than N individual writes. The following benchmark writes one thousand batches where each batch contains one thousand 100-byte values. TreeDB does not support batch writes and is omitted from this benchmark.</p>
<h4>Sequential Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">840,000 entries/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.08x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">124,000 entries/sec</td>
<td class="c3"><div class="bsql" style="width:52px">&nbsp;</div></td>
<td class="c4">(2.55x baseline)</td></tr>
</table>
<h4>Random Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">221,000 entries/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.35x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">22,000 entries/sec</td>
<td class="c3"><div class="bsql" style="width:34px">&nbsp;</div></td>
<td class="c4">(2.23x baseline)</td></tr>
</table>
<p>Because of the way LevelDB persistent storage is organized, batches of
random writes are not much slower (only a factor of 4x) than batches
of sequential writes.</p>
<h3>C. Synchronous Writes</h3>
<p>In the following benchmark, we enable the synchronous writing modes
of all of the databases. Since this change significantly slows down the
benchmark, we stop after 10,000 writes. For synchronous write tests, we've
disabled hard drive write-caching (using `hdparm -W 0 [device]`).</p>
<ul>
<li>For LevelDB, we set WriteOptions.sync = true.</li>
<li>In TreeDB, we enabled TreeDB's OAUTOSYNC option.</li>
<li>For SQLite3, we set "PRAGMA synchronous = FULL".</li>
</ul>
<h4>Sequential Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">100 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(0.003x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">7 ops/sec</td>
<td class="c3"><div class="bkct" style="width:27px">&nbsp;</div></td>
<td class="c4">(0.0004x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">88 ops/sec</td>
<td class="c3"><div class="bsql" style="width:315px">&nbsp;</div></td>
<td class="c4">(0.002x baseline)</td></tr>
</table>
<h4>Random Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">100 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(0.015x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">8 ops/sec</td>
<td class="c3"><div class="bkct" style="width:29px">&nbsp;</div></td>
<td class="c4">(0.001x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">88 ops/sec</td>
<td class="c3"><div class="bsql" style="width:314px">&nbsp;</div></td>
<td class="c4">(0.009x baseline)</td></tr>
</table>
<p>Also see the <code>ext4</code> performance numbers below
since synchronous writes behave significantly differently
on <code>ext3</code> and <code>ext4</code>.</p>
<h3>D. Turning Compression Off</h3>
<p>In the baseline measurements, LevelDB and TreeDB were using
light-weight compression
(<a href="http://code.google.com/p/snappy/">Snappy</a> for LevelDB,
and <a href="http://www.oberhumer.com/opensource/lzo/">LZO</a> for
TreeDB). SQLite3, by default does not use compression. The
experiments below show what happens when compression is disabled in
all of the databases (the SQLite3 numbers are just a copy of
its baseline measurements):</p>
<h4>Sequential Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">594,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(0.76x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">485,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:239px">&nbsp;</div></td>
<td class="c4">(1.42x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">48,600 ops/sec</td>
<td class="c3"><div class="bsql" style="width:29px">&nbsp;</div></td>
<td class="c4">(1.00x baseline)</td></tr>
</table>
<h4>Random Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">135,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:296px">&nbsp;</div></td>
<td class="c4">(0.82x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">159,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.80x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">9,860 ops/sec</td>
<td class="c3"><div class="bsql" style="width:22px">&nbsp;</div></td>
<td class="c4">(1.00x baseline)</td></tr>
</table>
<p>LevelDB's write performance is better with compression than without
since compression decreases the amount of data that has to be written
to disk. Therefore LevelDB users can leave compression enabled in
most scenarios without having worry about a tradeoff between space
usage and performance. TreeDB's performance on the other hand is
better without compression than with compression. Presumably this is
because TreeDB's compression library (LZO) is more expensive than
LevelDB's compression library (Snappy).<p>
<h3>E. Using More Memory</h3>
<p>We increased the overall cache size for each database to 128 MB. For LevelDB, we partitioned 128 MB into a 120 MB write buffer and 8 MB of cache (up from 2 MB of write buffer and 2 MB of cache). For SQLite3, we kept the page size at 1024 bytes, but increased the number of pages to 131,072 (up from 4096). For TreeDB, we also kept the page size at 1024 bytes, but increased the cache size to 128 MB (up from 4 MB).</p>
<h4>Sequential Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">812,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.04x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">321,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:138px">&nbsp;</div></td>
<td class="c4">(0.94x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">48,500 ops/sec</td>
<td class="c3"><div class="bsql" style="width:21px">&nbsp;</div></td>
<td class="c4">(1.00x baseline)</td></tr>
</table>
<h4>Random Writes</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">355,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(2.16x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">284,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:280px">&nbsp;</div></td>
<td class="c4">(3.21x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">9,670 ops/sec</td>
<td class="c3"><div class="bsql" style="width:10px">&nbsp;</div></td>
<td class="c4">(0.98x baseline)</td></tr>
</table>
<p>SQLite's performance does not change substantially when compared to
the baseline, but the random write performance for both LevelDB and
TreeDB increases significantly. LevelDB's performance improves
because a larger write buffer reduces the need to merge sorted files
(since it creates a smaller number of larger sorted files). TreeDB's
performance goes up because the entire database is available in memory
for fast in-place updates.</p>
<h2>3. Read Performance under Different Configurations</h2>
<h3>A. Larger Caches</h3>
<p>We increased the overall memory usage to 128 MB for each database.
For LevelDB, we allocated 8 MB to LevelDB's write buffer and 120 MB
to LevelDB's cache. The other databases don't differentiate between a
write buffer and a cache, so we simply set their cache size to 128
MB.</p>
<h4>Sequential Reads</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">5,210,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.29x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">1,070,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:72px">&nbsp;</div></td>
<td class="c4">(1.06x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">609,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:41px">&nbsp;</div></td>
<td class="c4">(1.59x baseline)</td></tr>
</table>
<h4>Random Reads</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">190,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:144px">&nbsp;</div></td>
<td class="c4">(1.47x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">463,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:350px">&nbsp;</div></td>
<td class="c4">(3.07x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">186,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:141px">&nbsp;</div></td>
<td class="c4">(1.39x baseline)</td></tr>
</table>
<p>As expected, the read performance of all of the databases increases
when the caches are enlarged. In particular, TreeDB seems to make
very effective use of a cache that is large enough to hold the entire
database.</p>
<h3>B. No Compression Reads </h3>
<p>For this benchmark, we populated a database with 1 million entries consisting of 16 byte keys and 100 byte values. We compiled LevelDB and Kyoto Cabinet without compression support, so results that are read out from the database are already uncompressed. We've listed the SQLite3 baseline read performance as a point of comparison.</p>
<h4>Sequential Reads</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">4,880,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.21x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">1,230,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:88px">&nbsp;</div></td>
<td class="c4">(3.60x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">383,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:27px">&nbsp;</div></td>
<td class="c4">(1.00x baseline)</td></tr>
</table>
<h4>Random Reads</h4>
<table class="bn">
<tr><td class="c1">LevelDB</td>
<td class="c2">149,000 ops/sec</td>
<td class="c3"><div class="bldb" style="width:300px">&nbsp;</div></td>
<td class="c4">(1.16x baseline)</td></tr>
<tr><td class="c1">Kyoto TreeDB</td>
<td class="c2">175,000 ops/sec</td>
<td class="c3"><div class="bkct" style="width:350px">&nbsp;</div></td>
<td class="c4">(1.16x baseline)</td></tr>
<tr><td class="c1">SQLite3</td>
<td class="c2">134,000 ops/sec</td>
<td class="c3"><div class="bsql" style="width:268px">&nbsp;</div></td>
<td class="c4">(1.00x baseline)</td></tr>
</table>
<p>Performance of both LevelDB and TreeDB improves a small amount when
compression is disabled. Note however that under different workloads,
performance may very well be better with compression if it allows more
of the working set to fit in memory.</p>
<h2>Note about Ext4 Filesystems</h2>
<p>The preceding numbers are for an ext3 file system. Synchronous writes are much slower under <a href="http://en.wikipedia.org/wiki/Ext4">ext4</a> (LevelDB drops to ~31 writes / second and TreeDB drops to ~5 writes / second; SQLite3's synchronous writes do not noticeably drop) due to ext4's different handling of <span class="code">fsync</span> / <span class="code">msync</span> calls. Even LevelDB's asynchronous write performance drops somewhat since it spreads its storage across multiple files and issues <span class="code">fsync</span> calls when switching to a new file.</p>
<h2>Acknowledgements</h2>
<p>Jeff Dean and Sanjay Ghemawat wrote LevelDB. Kevin Tseng wrote and compiled these benchmarks. Mikio Hirabayashi, Scott Hess, and Gabor Cselle provided help and advice.</p>
</body>
</html>
src/leveldb/doc/impl.md
+170
View File
@@ -0,0 +1,170 @@
## Files
The implementation of leveldb is similar in spirit to the representation of a
single [Bigtable tablet (section 5.3)](http://research.google.com/archive/bigtable.html).
However the organization of the files that make up the representation is
somewhat different and is explained below.
Each database is represented by a set of files stored in a directory. There are
several different types of files as documented below:
### Log files
A log file (*.log) stores a sequence of recent updates. Each update is appended
to the current log file. When the log file reaches a pre-determined size
(approximately 4MB by default), it is converted to a sorted table (see below)
and a new log file is created for future updates.
A copy of the current log file is kept in an in-memory structure (the
`memtable`). This copy is consulted on every read so that read operations
reflect all logged updates.
## Sorted tables
A sorted table (*.ldb) stores a sequence of entries sorted by key. Each entry is
either a value for the key, or a deletion marker for the key. (Deletion markers
are kept around to hide obsolete values present in older sorted tables).
The set of sorted tables are organized into a sequence of levels. The sorted
table generated from a log file is placed in a special **young** level (also
called level-0). When the number of young files exceeds a certain threshold
(currently four), all of the young files are merged together with all of the
overlapping level-1 files to produce a sequence of new level-1 files (we create
a new level-1 file for every 2MB of data.)
Files in the young level may contain overlapping keys. However files in other
levels have distinct non-overlapping key ranges. Consider level number L where
L >= 1. When the combined size of files in level-L exceeds (10^L) MB (i.e., 10MB
for level-1, 100MB for level-2, ...), one file in level-L, and all of the
overlapping files in level-(L+1) are merged to form a set of new files for
level-(L+1). These merges have the effect of gradually migrating new updates
from the young level to the largest level using only bulk reads and writes
(i.e., minimizing expensive seeks).
### Manifest
A MANIFEST file lists the set of sorted tables that make up each level, the
corresponding key ranges, and other important metadata. A new MANIFEST file
(with a new number embedded in the file name) is created whenever the database
is reopened. The MANIFEST file is formatted as a log, and changes made to the
serving state (as files are added or removed) are appended to this log.
### Current
CURRENT is a simple text file that contains the name of the latest MANIFEST
file.
### Info logs
Informational messages are printed to files named LOG and LOG.old.
### Others
Other files used for miscellaneous purposes may also be present (LOCK, *.dbtmp).
## Level 0
When the log file grows above a certain size (1MB by default):
Create a brand new memtable and log file and direct future updates here
In the background:
Write the contents of the previous memtable to an sstable
Discard the memtable
Delete the old log file and the old memtable
Add the new sstable to the young (level-0) level.
## Compactions
When the size of level L exceeds its limit, we compact it in a background
thread. The compaction picks a file from level L and all overlapping files from
the next level L+1. Note that if a level-L file overlaps only part of a
level-(L+1) file, the entire file at level-(L+1) is used as an input to the
compaction and will be discarded after the compaction. Aside: because level-0
is special (files in it may overlap each other), we treat compactions from
level-0 to level-1 specially: a level-0 compaction may pick more than one
level-0 file in case some of these files overlap each other.
A compaction merges the contents of the picked files to produce a sequence of
level-(L+1) files. We switch to producing a new level-(L+1) file after the
current output file has reached the target file size (2MB). We also switch to a
new output file when the key range of the current output file has grown enough
to overlap more than ten level-(L+2) files. This last rule ensures that a later
compaction of a level-(L+1) file will not pick up too much data from
level-(L+2).
The old files are discarded and the new files are added to the serving state.
Compactions for a particular level rotate through the key space. In more detail,
for each level L, we remember the ending key of the last compaction at level L.
The next compaction for level L will pick the first file that starts after this
key (wrapping around to the beginning of the key space if there is no such
file).
Compactions drop overwritten values. They also drop deletion markers if there
are no higher numbered levels that contain a file whose range overlaps the
current key.
### Timing
Level-0 compactions will read up to four 1MB files from level-0, and at worst
all the level-1 files (10MB). I.e., we will read 14MB and write 14MB.
Other than the special level-0 compactions, we will pick one 2MB file from level
L. In the worst case, this will overlap ~ 12 files from level L+1 (10 because
level-(L+1) is ten times the size of level-L, and another two at the boundaries
since the file ranges at level-L will usually not be aligned with the file
ranges at level-L+1). The compaction will therefore read 26MB and write 26MB.
Assuming a disk IO rate of 100MB/s (ballpark range for modern drives), the worst
compaction cost will be approximately 0.5 second.
If we throttle the background writing to something small, say 10% of the full
100MB/s speed, a compaction may take up to 5 seconds. If the user is writing at
10MB/s, we might build up lots of level-0 files (~50 to hold the 5*10MB). This
may significantly increase the cost of reads due to the overhead of merging more
files together on every read.
Solution 1: To reduce this problem, we might want to increase the log switching
threshold when the number of level-0 files is large. Though the downside is that
the larger this threshold, the more memory we will need to hold the
corresponding memtable.
Solution 2: We might want to decrease write rate artificially when the number of
level-0 files goes up.
Solution 3: We work on reducing the cost of very wide merges. Perhaps most of
the level-0 files will have their blocks sitting uncompressed in the cache and
we will only need to worry about the O(N) complexity in the merging iterator.
### Number of files
Instead of always making 2MB files, we could make larger files for larger levels
to reduce the total file count, though at the expense of more bursty
compactions. Alternatively, we could shard the set of files into multiple
directories.
An experiment on an ext3 filesystem on Feb 04, 2011 shows the following timings
to do 100K file opens in directories with varying number of files:
| Files in directory | Microseconds to open a file |
|-------------------:|----------------------------:|
| 1000 | 9 |
| 10000 | 10 |
| 100000 | 16 |
So maybe even the sharding is not necessary on modern filesystems?
## Recovery
* Read CURRENT to find name of the latest committed MANIFEST
* Read the named MANIFEST file
* Clean up stale files
* We could open all sstables here, but it is probably better to be lazy...
* Convert log chunk to a new level-0 sstable
* Start directing new writes to a new log file with recovered sequence#
## Garbage collection of files
`DeleteObsoleteFiles()` is called at the end of every compaction and at the end
of recovery. It finds the names of all files in the database. It deletes all log
files that are not the current log file. It deletes all table files that are not
referenced from some level and are not the output of an active compaction.
src/leveldb/doc/index.md
+523
View File
@@ -0,0 +1,523 @@
leveldb
=======
_Jeff Dean, Sanjay Ghemawat_
The leveldb library provides a persistent key value store. Keys and values are
arbitrary byte arrays. The keys are ordered within the key value store
according to a user-specified comparator function.
## Opening A Database
A leveldb database has a name which corresponds to a file system directory. All
of the contents of database are stored in this directory. The following example
shows how to open a database, creating it if necessary:
```c++
#include <cassert>
#include "leveldb/db.h"
leveldb::DB* db;
leveldb::Options options;
options.create_if_missing = true;
leveldb::Status status = leveldb::DB::Open(options, "/tmp/testdb", &db);
assert(status.ok());
...
```
If you want to raise an error if the database already exists, add the following
line before the `leveldb::DB::Open` call:
```c++
options.error_if_exists = true;
```
## Status
You may have noticed the `leveldb::Status` type above. Values of this type are
returned by most functions in leveldb that may encounter an error. You can check
if such a result is ok, and also print an associated error message:
```c++
leveldb::Status s = ...;
if (!s.ok()) cerr << s.ToString() << endl;
```
## Closing A Database
When you are done with a database, just delete the database object. Example:
```c++
... open the db as described above ...
... do something with db ...
delete db;
```
## Reads And Writes
The database provides Put, Delete, and Get methods to modify/query the database.
For example, the following code moves the value stored under key1 to key2.
```c++
std::string value;
leveldb::Status s = db->Get(leveldb::ReadOptions(), key1, &value);
if (s.ok()) s = db->Put(leveldb::WriteOptions(), key2, value);
if (s.ok()) s = db->Delete(leveldb::WriteOptions(), key1);
```
## Atomic Updates
Note that if the process dies after the Put of key2 but before the delete of
key1, the same value may be left stored under multiple keys. Such problems can
be avoided by using the `WriteBatch` class to atomically apply a set of updates:
```c++
#include "leveldb/write_batch.h"
...
std::string value;
leveldb::Status s = db->Get(leveldb::ReadOptions(), key1, &value);
if (s.ok()) {
leveldb::WriteBatch batch;
batch.Delete(key1);
batch.Put(key2, value);
s = db->Write(leveldb::WriteOptions(), &batch);
}
```
The `WriteBatch` holds a sequence of edits to be made to the database, and these
edits within the batch are applied in order. Note that we called Delete before
Put so that if key1 is identical to key2, we do not end up erroneously dropping
the value entirely.
Apart from its atomicity benefits, `WriteBatch` may also be used to speed up
bulk updates by placing lots of individual mutations into the same batch.
## Synchronous Writes
By default, each write to leveldb is asynchronous: it returns after pushing the
write from the process into the operating system. The transfer from operating
system memory to the underlying persistent storage happens asynchronously. The
sync flag can be turned on for a particular write to make the write operation
not return until the data being written has been pushed all the way to
persistent storage. (On Posix systems, this is implemented by calling either
`fsync(...)` or `fdatasync(...)` or `msync(..., MS_SYNC)` before the write
operation returns.)
```c++
leveldb::WriteOptions write_options;
write_options.sync = true;
db->Put(write_options, ...);
```
Asynchronous writes are often more than a thousand times as fast as synchronous
writes. The downside of asynchronous writes is that a crash of the machine may
cause the last few updates to be lost. Note that a crash of just the writing
process (i.e., not a reboot) will not cause any loss since even when sync is
false, an update is pushed from the process memory into the operating system
before it is considered done.
Asynchronous writes can often be used safely. For example, when loading a large
amount of data into the database you can handle lost updates by restarting the
bulk load after a crash. A hybrid scheme is also possible where every Nth write
is synchronous, and in the event of a crash, the bulk load is restarted just
after the last synchronous write finished by the previous run. (The synchronous
write can update a marker that describes where to restart on a crash.)
`WriteBatch` provides an alternative to asynchronous writes. Multiple updates
may be placed in the same WriteBatch and applied together using a synchronous
write (i.e., `write_options.sync` is set to true). The extra cost of the
synchronous write will be amortized across all of the writes in the batch.
## Concurrency
A database may only be opened by one process at a time. The leveldb
implementation acquires a lock from the operating system to prevent misuse.
Within a single process, the same `leveldb::DB` object may be safely shared by
multiple concurrent threads. I.e., different threads may write into or fetch
iterators or call Get on the same database without any external synchronization
(the leveldb implementation will automatically do the required synchronization).
However other objects (like Iterator and `WriteBatch`) may require external
synchronization. If two threads share such an object, they must protect access
to it using their own locking protocol. More details are available in the public
header files.
## Iteration
The following example demonstrates how to print all key,value pairs in a
database.
```c++
leveldb::Iterator* it = db->NewIterator(leveldb::ReadOptions());
for (it->SeekToFirst(); it->Valid(); it->Next()) {
cout << it->key().ToString() << ": " << it->value().ToString() << endl;
}
assert(it->status().ok()); // Check for any errors found during the scan
delete it;
```
The following variation shows how to process just the keys in the range
[start,limit):
```c++
for (it->Seek(start);
it->Valid() && it->key().ToString() < limit;
it->Next()) {
...
}
```
You can also process entries in reverse order. (Caveat: reverse iteration may be
somewhat slower than forward iteration.)
```c++
for (it->SeekToLast(); it->Valid(); it->Prev()) {
...
}
```
## Snapshots
Snapshots provide consistent read-only views over the entire state of the
key-value store. `ReadOptions::snapshot` may be non-NULL to indicate that a
read should operate on a particular version of the DB state. If
`ReadOptions::snapshot` is NULL, the read will operate on an implicit snapshot
of the current state.
Snapshots are created by the `DB::GetSnapshot()` method:
```c++
leveldb::ReadOptions options;
options.snapshot = db->GetSnapshot();
... apply some updates to db ...
leveldb::Iterator* iter = db->NewIterator(options);
... read using iter to view the state when the snapshot was created ...
delete iter;
db->ReleaseSnapshot(options.snapshot);
```
Note that when a snapshot is no longer needed, it should be released using the
`DB::ReleaseSnapshot` interface. This allows the implementation to get rid of
state that was being maintained just to support reading as of that snapshot.
## Slice
The return value of the `it->key()` and `it->value()` calls above are instances
of the `leveldb::Slice` type. Slice is a simple structure that contains a length
and a pointer to an external byte array. Returning a Slice is a cheaper
alternative to returning a `std::string` since we do not need to copy
potentially large keys and values. In addition, leveldb methods do not return
null-terminated C-style strings since leveldb keys and values are allowed to
contain `'\0'` bytes.
C++ strings and null-terminated C-style strings can be easily converted to a
Slice:
```c++
leveldb::Slice s1 = "hello";
std::string str("world");
leveldb::Slice s2 = str;
```
A Slice can be easily converted back to a C++ string:
```c++
std::string str = s1.ToString();
assert(str == std::string("hello"));
```
Be careful when using Slices since it is up to the caller to ensure that the
external byte array into which the Slice points remains live while the Slice is
in use. For example, the following is buggy:
```c++
leveldb::Slice slice;
if (...) {
std::string str = ...;
slice = str;
}
Use(slice);
```
When the if statement goes out of scope, str will be destroyed and the backing
storage for slice will disappear.
## Comparators
The preceding examples used the default ordering function for key, which orders
bytes lexicographically. You can however supply a custom comparator when opening
a database. For example, suppose each database key consists of two numbers and
we should sort by the first number, breaking ties by the second number. First,
define a proper subclass of `leveldb::Comparator` that expresses these rules:
```c++
class TwoPartComparator : public leveldb::Comparator {
public:
// Three-way comparison function:
// if a < b: negative result
// if a > b: positive result
// else: zero result
int Compare(const leveldb::Slice& a, const leveldb::Slice& b) const {
int a1, a2, b1, b2;
ParseKey(a, &a1, &a2);
ParseKey(b, &b1, &b2);
if (a1 < b1) return -1;
if (a1 > b1) return +1;
if (a2 < b2) return -1;
if (a2 > b2) return +1;
return 0;
}
// Ignore the following methods for now:
const char* Name() const { return "TwoPartComparator"; }
void FindShortestSeparator(std::string*, const leveldb::Slice&) const {}
void FindShortSuccessor(std::string*) const {}
};
```
Now create a database using this custom comparator:
```c++
TwoPartComparator cmp;
leveldb::DB* db;
leveldb::Options options;
options.create_if_missing = true;
options.comparator = &cmp;
leveldb::Status status = leveldb::DB::Open(options, "/tmp/testdb", &db);
...
```
### Backwards compatibility
The result of the comparator's Name method is attached to the database when it
is created, and is checked on every subsequent database open. If the name
changes, the `leveldb::DB::Open` call will fail. Therefore, change the name if
and only if the new key format and comparison function are incompatible with
existing databases, and it is ok to discard the contents of all existing
databases.
You can however still gradually evolve your key format over time with a little
bit of pre-planning. For example, you could store a version number at the end of
each key (one byte should suffice for most uses). When you wish to switch to a
new key format (e.g., adding an optional third part to the keys processed by
`TwoPartComparator`), (a) keep the same comparator name (b) increment the
version number for new keys (c) change the comparator function so it uses the
version numbers found in the keys to decide how to interpret them.
## Performance
Performance can be tuned by changing the default values of the types defined in
`include/leveldb/options.h`.
### Block size
leveldb groups adjacent keys together into the same block and such a block is
the unit of transfer to and from persistent storage. The default block size is
approximately 4096 uncompressed bytes. Applications that mostly do bulk scans
over the contents of the database may wish to increase this size. Applications
that do a lot of point reads of small values may wish to switch to a smaller
block size if performance measurements indicate an improvement. There isn't much
benefit in using blocks smaller than one kilobyte, or larger than a few
megabytes. Also note that compression will be more effective with larger block
sizes.
### Compression
Each block is individually compressed before being written to persistent
storage. Compression is on by default since the default compression method is
very fast, and is automatically disabled for uncompressible data. In rare cases,
applications may want to disable compression entirely, but should only do so if
benchmarks show a performance improvement:
```c++
leveldb::Options options;
options.compression = leveldb::kNoCompression;
... leveldb::DB::Open(options, name, ...) ....
```
### Cache
The contents of the database are stored in a set of files in the filesystem and
each file stores a sequence of compressed blocks. If options.cache is non-NULL,
it is used to cache frequently used uncompressed block contents.
```c++
#include "leveldb/cache.h"
leveldb::Options options;
options.cache = leveldb::NewLRUCache(100 * 1048576); // 100MB cache
leveldb::DB* db;
leveldb::DB::Open(options, name, &db);
... use the db ...
delete db
delete options.cache;
```
Note that the cache holds uncompressed data, and therefore it should be sized
according to application level data sizes, without any reduction from
compression. (Caching of compressed blocks is left to the operating system
buffer cache, or any custom Env implementation provided by the client.)
When performing a bulk read, the application may wish to disable caching so that
the data processed by the bulk read does not end up displacing most of the
cached contents. A per-iterator option can be used to achieve this:
```c++
leveldb::ReadOptions options;
options.fill_cache = false;
leveldb::Iterator* it = db->NewIterator(options);
for (it->SeekToFirst(); it->Valid(); it->Next()) {
...
}
```
### Key Layout
Note that the unit of disk transfer and caching is a block. Adjacent keys
(according to the database sort order) will usually be placed in the same block.
Therefore the application can improve its performance by placing keys that are
accessed together near each other and placing infrequently used keys in a
separate region of the key space.
For example, suppose we are implementing a simple file system on top of leveldb.
The types of entries we might wish to store are:
filename -> permission-bits, length, list of file_block_ids
file_block_id -> data
We might want to prefix filename keys with one letter (say '/') and the
`file_block_id` keys with a different letter (say '0') so that scans over just
the metadata do not force us to fetch and cache bulky file contents.
### Filters
Because of the way leveldb data is organized on disk, a single `Get()` call may
involve multiple reads from disk. The optional FilterPolicy mechanism can be
used to reduce the number of disk reads substantially.
```c++
leveldb::Options options;
options.filter_policy = NewBloomFilterPolicy(10);
leveldb::DB* db;
leveldb::DB::Open(options, "/tmp/testdb", &db);
... use the database ...
delete db;
delete options.filter_policy;
```
The preceding code associates a Bloom filter based filtering policy with the
database. Bloom filter based filtering relies on keeping some number of bits of
data in memory per key (in this case 10 bits per key since that is the argument
we passed to `NewBloomFilterPolicy`). This filter will reduce the number of
unnecessary disk reads needed for Get() calls by a factor of approximately
a 100. Increasing the bits per key will lead to a larger reduction at the cost
of more memory usage. We recommend that applications whose working set does not
fit in memory and that do a lot of random reads set a filter policy.
If you are using a custom comparator, you should ensure that the filter policy
you are using is compatible with your comparator. For example, consider a
comparator that ignores trailing spaces when comparing keys.
`NewBloomFilterPolicy` must not be used with such a comparator. Instead, the
application should provide a custom filter policy that also ignores trailing
spaces. For example:
```c++
class CustomFilterPolicy : public leveldb::FilterPolicy {
private:
FilterPolicy* builtin_policy_;
public:
CustomFilterPolicy() : builtin_policy_(NewBloomFilterPolicy(10)) {}
~CustomFilterPolicy() { delete builtin_policy_; }
const char* Name() const { return "IgnoreTrailingSpacesFilter"; }
void CreateFilter(const Slice* keys, int n, std::string* dst) const {
// Use builtin bloom filter code after removing trailing spaces
std::vector<Slice> trimmed(n);
for (int i = 0; i < n; i++) {
trimmed[i] = RemoveTrailingSpaces(keys[i]);
}
return builtin_policy_->CreateFilter(&trimmed[i], n, dst);
}
};
```
Advanced applications may provide a filter policy that does not use a bloom
filter but uses some other mechanism for summarizing a set of keys. See
`leveldb/filter_policy.h` for detail.
## Checksums
leveldb associates checksums with all data it stores in the file system. There
are two separate controls provided over how aggressively these checksums are
verified:
`ReadOptions::verify_checksums` may be set to true to force checksum
verification of all data that is read from the file system on behalf of a
particular read. By default, no such verification is done.
`Options::paranoid_checks` may be set to true before opening a database to make
the database implementation raise an error as soon as it detects an internal
corruption. Depending on which portion of the database has been corrupted, the
error may be raised when the database is opened, or later by another database
operation. By default, paranoid checking is off so that the database can be used
even if parts of its persistent storage have been corrupted.
If a database is corrupted (perhaps it cannot be opened when paranoid checking
is turned on), the `leveldb::RepairDB` function may be used to recover as much
of the data as possible
## Approximate Sizes
The `GetApproximateSizes` method can used to get the approximate number of bytes
of file system space used by one or more key ranges.
```c++
leveldb::Range ranges[2];
ranges[0] = leveldb::Range("a", "c");
ranges[1] = leveldb::Range("x", "z");
uint64_t sizes[2];
leveldb::Status s = db->GetApproximateSizes(ranges, 2, sizes);
```
The preceding call will set `sizes[0]` to the approximate number of bytes of
file system space used by the key range `[a..c)` and `sizes[1]` to the
approximate number of bytes used by the key range `[x..z)`.
## Environment
All file operations (and other operating system calls) issued by the leveldb
implementation are routed through a `leveldb::Env` object. Sophisticated clients
may wish to provide their own Env implementation to get better control.
For example, an application may introduce artificial delays in the file IO
paths to limit the impact of leveldb on other activities in the system.
```c++
class SlowEnv : public leveldb::Env {
... implementation of the Env interface ...
};
SlowEnv env;
leveldb::Options options;
options.env = &env;
Status s = leveldb::DB::Open(options, ...);
```
## Porting
leveldb may be ported to a new platform by providing platform specific
implementations of the types/methods/functions exported by
`leveldb/port/port.h`. See `leveldb/port/port_example.h` for more details.
In addition, the new platform may need a new default `leveldb::Env`
implementation. See `leveldb/util/env_posix.h` for an example.
## Other Information
Details about the leveldb implementation may be found in the following
documents:
1. [Implementation notes](impl.md)
2. [Format of an immutable Table file](table_format.md)
3. [Format of a log file](log_format.md)
src/leveldb/doc/log_format.md
+75
View File
@@ -0,0 +1,75 @@
leveldb Log format
==================
The log file contents are a sequence of 32KB blocks. The only exception is that
the tail of the file may contain a partial block.
Each block consists of a sequence of records:
block := record* trailer?
record :=
checksum: uint32 // crc32c of type and data[] ; little-endian
length: uint16 // little-endian
type: uint8 // One of FULL, FIRST, MIDDLE, LAST
data: uint8[length]
A record never starts within the last six bytes of a block (since it won't fit).
Any leftover bytes here form the trailer, which must consist entirely of zero
bytes and must be skipped by readers.
Aside: if exactly seven bytes are left in the current block, and a new non-zero
length record is added, the writer must emit a FIRST record (which contains zero
bytes of user data) to fill up the trailing seven bytes of the block and then
emit all of the user data in subsequent blocks.
More types may be added in the future. Some Readers may skip record types they
do not understand, others may report that some data was skipped.
FULL == 1
FIRST == 2
MIDDLE == 3
LAST == 4
The FULL record contains the contents of an entire user record.
FIRST, MIDDLE, LAST are types used for user records that have been split into
multiple fragments (typically because of block boundaries). FIRST is the type
of the first fragment of a user record, LAST is the type of the last fragment of
a user record, and MIDDLE is the type of all interior fragments of a user
record.
Example: consider a sequence of user records:
A: length 1000
B: length 97270
C: length 8000
**A** will be stored as a FULL record in the first block.
**B** will be split into three fragments: first fragment occupies the rest of
the first block, second fragment occupies the entirety of the second block, and
the third fragment occupies a prefix of the third block. This will leave six
bytes free in the third block, which will be left empty as the trailer.
**C** will be stored as a FULL record in the fourth block.
----
## Some benefits over the recordio format:
1. We do not need any heuristics for resyncing - just go to next block boundary
and scan. If there is a corruption, skip to the next block. As a
side-benefit, we do not get confused when part of the contents of one log
file are embedded as a record inside another log file.
2. Splitting at approximate boundaries (e.g., for mapreduce) is simple: find the
next block boundary and skip records until we hit a FULL or FIRST record.
3. We do not need extra buffering for large records.
## Some downsides compared to recordio format:
1. No packing of tiny records. This could be fixed by adding a new record type,
so it is a shortcoming of the current implementation, not necessarily the
format.
2. No compression. Again, this could be fixed by adding new record types.
src/leveldb/doc/table_format.md
+107
View File
@@ -0,0 +1,107 @@
leveldb File format
===================
<beginning_of_file>
[data block 1]
[data block 2]
...
[data block N]
[meta block 1]
...
[meta block K]
[metaindex block]
[index block]
[Footer] (fixed size; starts at file_size - sizeof(Footer))
<end_of_file>
The file contains internal pointers. Each such pointer is called
a BlockHandle and contains the following information:
offset: varint64
size: varint64
See [varints](https://developers.google.com/protocol-buffers/docs/encoding#varints)
for an explanation of varint64 format.
1. The sequence of key/value pairs in the file are stored in sorted
order and partitioned into a sequence of data blocks. These blocks
come one after another at the beginning of the file. Each data block
is formatted according to the code in `block_builder.cc`, and then
optionally compressed.
2. After the data blocks we store a bunch of meta blocks. The
supported meta block types are described below. More meta block types
may be added in the future. Each meta block is again formatted using
`block_builder.cc` and then optionally compressed.
3. A "metaindex" block. It contains one entry for every other meta
block where the key is the name of the meta block and the value is a
BlockHandle pointing to that meta block.
4. An "index" block. This block contains one entry per data block,
where the key is a string >= last key in that data block and before
the first key in the successive data block. The value is the
BlockHandle for the data block.
5. At the very end of the file is a fixed length footer that contains
the BlockHandle of the metaindex and index blocks as well as a magic number.
metaindex_handle: char[p]; // Block handle for metaindex
index_handle: char[q]; // Block handle for index
padding: char[40-p-q];// zeroed bytes to make fixed length
// (40==2*BlockHandle::kMaxEncodedLength)
magic: fixed64; // == 0xdb4775248b80fb57 (little-endian)
## "filter" Meta Block
If a `FilterPolicy` was specified when the database was opened, a
filter block is stored in each table. The "metaindex" block contains
an entry that maps from `filter.<N>` to the BlockHandle for the filter
block where `<N>` is the string returned by the filter policy's
`Name()` method.
The filter block stores a sequence of filters, where filter i contains
the output of `FilterPolicy::CreateFilter()` on all keys that are stored
in a block whose file offset falls within the range
[ i*base ... (i+1)*base-1 ]
Currently, "base" is 2KB. So for example, if blocks X and Y start in
the range `[ 0KB .. 2KB-1 ]`, all of the keys in X and Y will be
converted to a filter by calling `FilterPolicy::CreateFilter()`, and the
resulting filter will be stored as the first filter in the filter
block.
The filter block is formatted as follows:
[filter 0]
[filter 1]
[filter 2]
...
[filter N-1]
[offset of filter 0] : 4 bytes
[offset of filter 1] : 4 bytes
[offset of filter 2] : 4 bytes
...
[offset of filter N-1] : 4 bytes
[offset of beginning of offset array] : 4 bytes
lg(base) : 1 byte
The offset array at the end of the filter block allows efficient
mapping from a data block offset to the corresponding filter.
## "stats" Meta Block
This meta block contains a bunch of stats. The key is the name
of the statistic. The value contains the statistic.
TODO(postrelease): record following stats.
data size
index size
key size (uncompressed)
value size (uncompressed)
number of entries
number of data blocks