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Posted to common-commits@hadoop.apache.org by wh...@apache.org on 2016/01/05 20:52:22 UTC
[22/50] [abbrv] hadoop git commit: [partial-ns] Import HDFSDB.
http://git-wip-us.apache.org/repos/asf/hadoop/blob/4a6419f4/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_sqlite3.cc
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diff --git a/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_sqlite3.cc b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_sqlite3.cc
new file mode 100644
index 0000000..e63aaa8
--- /dev/null
+++ b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_sqlite3.cc
@@ -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;
+}
http://git-wip-us.apache.org/repos/asf/hadoop/blob/4a6419f4/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_tree_db.cc
----------------------------------------------------------------------
diff --git a/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_tree_db.cc b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_tree_db.cc
new file mode 100644
index 0000000..ed86f03
--- /dev/null
+++ b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/bench/db_bench_tree_db.cc
@@ -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);
+
+ } 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;
+}
http://git-wip-us.apache.org/repos/asf/hadoop/blob/4a6419f4/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/benchmark.html
----------------------------------------------------------------------
diff --git a/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/benchmark.html b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/benchmark.html
new file mode 100644
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--- /dev/null
+++ b/hadoop-hdfs-project/hadoop-hdfsdb/src/main/native/hdfsdb/doc/benchmark.html
@@ -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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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"> </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>
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+ margin-left: 0.5in;
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+ color: black;
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+ margin-left: -0.2in;
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+ margin-left: -0in;
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+ margin-left: -0in;
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+h4 {
+ margin-left: -0in;
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+hr {
+ margin-left: -0in;
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+
+/* Definition lists: definition term bold */
+dt {
+ font-weight: bold;
+}
+
+address {
+ text-align: center;
+}
+code,samp,var {
+ color: blue;
+}
+kbd {
+ color: #600000;
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+div.note p {
+ float: right;
+ width: 3in;
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+<html>
+<head>
+<link rel="stylesheet" type="text/css" href="doc.css" />
+<title>Leveldb file layout and compactions</title>
+</head>
+
+<body>
+
+<h1>Files</h1>
+
+The implementation of leveldb is similar in spirit to the
+representation of a single
+<a href="http://research.google.com/archive/bigtable.html">
+Bigtable tablet (section 5.3)</a>.
+However the organization of the files that make up the representation
+is somewhat different and is explained below.
+
+<p>
+Each database is represented by a set of files stored in a directory.
+There are several different types of files as documented below:
+<p>
+<h2>Log files</h2>
+<p>
+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.
+<p>
+A copy of the current log file is kept in an in-memory structure (the
+<code>memtable</code>). This copy is consulted on every read so that read
+operations reflect all logged updates.
+<p>
+<h2>Sorted tables</h2>
+<p>
+A sorted table (*.sst) 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).
+<p>
+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 <code>young</code>
+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.)
+<p>
+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).
+
+<h2>Manifest</h2>
+<p>
+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.
+<p>
+<h2>Current</h2>
+<p>
+CURRENT is a simple text file that contains the name of the latest
+MANIFEST file.
+<p>
+<h2>Info logs</h2>
+<p>
+Informational messages are printed to files named LOG and LOG.old.
+<p>
+<h2>Others</h2>
+<p>
+Other files used for miscellaneous purposes may also be present
+(LOCK, *.dbtmp).
+
+<h1>Level 0</h1>
+When the log file grows above a certain size (1MB by default):
+<ul>
+<li>Create a brand new memtable and log file and direct future updates here
+<li>In the background:
+<ul>
+<li>Write the contents of the previous memtable to an sstable
+<li>Discard the memtable
+<li>Delete the old log file and the old memtable
+<li>Add the new sstable to the young (level-0) level.
+</ul>
+</ul>
+
+<h1>Compactions</h1>
+
+<p>
+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.
+
+<p>
+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 then
+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).
+
+<p>
+The old files are discarded and the new files are added to the serving
+state.
+
+<p>
+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).
+
+<p>
+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.
+
+<h2>Timing</h2>
+
+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.
+
+<p>
+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.
+
+<p>
+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 signficantly increase the cost of
+reads due to the overhead of merging more files together on every
+read.
+
+<p>
+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.
+
+<p>
+Solution 2: We might want to decrease write rate artificially when the
+number of level-0 files goes up.
+
+<p>
+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.
+
+<h2>Number of files</h2>
+
+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.
+
+<p>
+An experiment on an <code>ext3</code> filesystem on Feb 04, 2011 shows
+the following timings to do 100K file opens in directories with
+varying number of files:
+<table class="datatable">
+<tr><th>Files in directory</th><th>Microseconds to open a file</th></tr>
+<tr><td>1000</td><td>9</td>
+<tr><td>10000</td><td>10</td>
+<tr><td>100000</td><td>16</td>
+</table>
+So maybe even the sharding is not necessary on modern filesystems?
+
+<h1>Recovery</h1>
+
+<ul>
+<li> Read CURRENT to find name of the latest committed MANIFEST
+<li> Read the named MANIFEST file
+<li> Clean up stale files
+<li> We could open all sstables here, but it is probably better to be lazy...
+<li> Convert log chunk to a new level-0 sstable
+<li> Start directing new writes to a new log file with recovered sequence#
+</ul>
+
+<h1>Garbage collection of files</h1>
+
+<code>DeleteObsoleteFiles()</code> 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.
+
+</body>
+</html>