# Performance

Performance Comparison
PolePosition Benchmark
Application Profiling
Database Profiling
Database Performance Tuning
Using the Built-In Profiler
Fast Database Import

## Performance Comparison

In many cases H2 is faster than other (open source and not open source) database engines. Please note this is mostly a single connection benchmark run on one computer.

### Embedded

Test Case | Unit | H2 | HSQLDB | Derby
--- | --- | --- | --- | ---
Simple: Init | ms | 241 | 431 | 1027
Simple: Query (random) | ms | 193 | 267 | 748
Simple: Query (sequential) | ms | 89 | 179 | 658
Simple: Update (random) | ms | 406 | 772 | 12175
Simple: Delete (sequential) | ms | 155 | 266 | 6281
Simple: Memory Usage | MB | 7 | 13 | 16
BenchA: Init | ms | 200 | 251 | 1075
BenchA: Transactions | ms | 1071 | 1458 | 8142
BenchA: Memory Usage | MB | 8 | 14 | 12
BenchB: Init | ms | 787 | 1584 | 4163
BenchB: Transactions | ms | 465 | 875 | 2744
BenchB: Memory Usage | MB | 17 | 13 | 10
BenchC: Init | ms | 348 | 225 | 922
BenchC: Transactions | ms | 1382 | 865 | 3527
BenchC: Memory Usage | MB | 12 | 20 | 11
Executed statements | # | 322929 | 322929 | 322929
Total time | ms | 5337 | 7173 | 41462
Statements per second | # | 60507 | 45020 | 7788

### Client-Server

Test Case | Unit | H2 | HSQLDB | Derby | PostgreSQL | MySQL
--- | --- | --- | --- | --- | --- | ---
Simple: Init | ms | 1715 | 2096 | 3008 | 3093 | 3084
Simple: Query (random) | ms | 2615 | 2119 | 4450 | 3201 | 3313
Simple: Query (sequential) | ms | 2531 | 1944 | 4019 | 3163 | 3295
Simple: Update (random) | ms | 1862 | 2486 | 13929 | 4404 | 4391
Simple: Delete (sequential) | ms | 778 | 1118 | 7032 | 1682 | 1882
Simple: Memory Usage | MB | 8 | 14 | 18 | 1 | 2
BenchA: Init | ms | 1264 | 1686 | 2734 | 2867 | 3225
BenchA: Transactions | ms | 5998 | 6829 | 14323 | 11491 | 10571
BenchA: Memory Usage | MB | 9 | 18 | 14 | 1 | 2
BenchB: Init | ms | 5571 | 7553 | 11636 | 12226 | 12553
BenchB: Transactions | ms | 1931 | 3417 | 3435 | 2407 | 2149
BenchB: Memory Usage | MB | 18 | 16 | 13 | 2 | 2
BenchC: Init | ms | 1333 | 968 | 1769 | 1693 | 2645
BenchC: Transactions | ms | 6508 | 4330 | 7910 | 7564 | 6108
BenchC: Memory Usage | MB | 12 | 21 | 14 | 2 | 2
Executed statements | # | 322929 | 322929 | 322929 | 322929 | 322929
Total time | ms | 32106 | 34546 | 74245 | 53791 | 53216
Statements per second | # | 10058 | 9347 | 4349 | 6003 | 6068

### Benchmark Results and Comments

#### H2

Version 1.2.137 (2010-06-06) was used for the test. For most operations, the performance of H2 is about the same as for HSQLDB. One situation where H2 is slow is large result sets, because they are buffered to disk if more than a certain number of records are returned. The advantage of buffering is: there is no limit on the result set size. The database open/close time is fixed.

#### HSQLDB

Version 2.0.0 was used for the test. Cached tables are used in this test (`hsqldb.default_table_type=cached`), and the write delay is 1 second (`SET WRITE_DELAY 1`). Older versions of HSQLDB (pre 2.0) where very slow in the last test (BenchC: Transactions).

A disadvantage of HSQLDB is the slow startup / shutdown time (currently not listed) when using bigger databases. The reason is, a backup of the whole data is made whenever the database is opened or closed.

#### Derby

Version 10.6.1.0 was used for the test. Derby is clearly the slowest embedded database in this test. This seems to be a structural problem, because all operations are really slow. It will be hard for the developers of Derby to improve the performance to a reasonable level. A few problems have been identified: leaving autocommit on is a problem for Derby. If it is switched off during the whole test, the results are about 20% better for Derby. Derby calls `FileChannel.force(false)`, but only twice per log file (not on each commit). Disabling this call improves performance for Derby by about 2%. Unlike H2, Derby does not call `FileDescriptor.sync()` on each checkpoint. Derby supports a testing mode (system property `derby.system.durability=test`) where durability is disabled. According to the documentation, this setting should be used for testing only, as the database may not recover after a crash. Enabling this setting improves performance by a factor of 2.6 (embedded mode) or 1.4 (server mode). Even if enabled, Derby is still less than half as fast as H2 in default mode.

#### PostgreSQL

Version 8.4.4 was used for the test. The following options where changed in `postgresql.conf: fsync = off, commit_delay = 1000`. PostgreSQL is run in server mode. The memory usage number is incorrect, because only the memory usage of the JDBC driver is measured.

#### MySQL

Version 5.1.47-community was used for the test. MySQL was run with the InnoDB backend. The setting `innodb_flush_log_at_trx_commit` (found in the `my.ini / my.cnf` file) was set to 0. Otherwise (and by default), MySQL is slow (around 140 statements per second in this test) because it tries to flush the data to disk for each commit. For small transactions (when autocommit is on) this is really slow. But many use cases use small or relatively small transactions. Too bad this setting is not listed in the configuration wizard, and it always overwritten when using the wizard. You need to change this setting manually in the file `my.ini / my.cnf`, and then restart the service. The memory usage number is incorrect, because only the memory usage of the JDBC driver is measured.

#### Firebird

Firebird 1.5 (default installation) was tested, but the results are not published currently. It is possible to run the performance test with the Firebird database, and any information on how to configure Firebird for higher performance are welcome.

#### Why Oracle / MS SQL Server / DB2 are Not Listed

The license of these databases does not allow to publish benchmark results. This doesn't mean that they are fast. They are in fact quite slow, and need a lot of memory. But you will need to test this yourself. SQLite was not tested because the JDBC driver doesn't support transactions.

### About this Benchmark

#### How to Run

This test was as follows:

    build benchmark

#### Separate Process per Database

For each database, a new process is started, to ensure the previous test does not impact the current test.

#### Number of Connections

This is mostly a single-connection benchmark. BenchB uses multiple connections; the other tests use one connection.

#### Real-World Tests

Good benchmarks emulate real-world use cases. This benchmark includes 4 test cases: BenchSimple uses one table and many small updates / deletes. BenchA is similar to the TPC-A test, but single connection / single threaded (see also: www.tpc.org). BenchB is similar to the TPC-B test, using multiple connections (one thread per connection). BenchC is similar to the TPC-C test, but single connection / single threaded.

#### Comparing Embedded with Server Databases

This is mainly a benchmark for embedded databases (where the application runs in the same virtual machine as the database engine). However MySQL and PostgreSQL are not Java databases and cannot be embedded into a Java application. For the Java databases, both embedded and server modes are tested.

#### Test Platform

This test is run on Mac OS X 10.6. No virus scanner was used, and disk indexing was disabled. The VM used is Sun JDK 1.6.

#### Multiple Runs

When a Java benchmark is run first, the code is not fully compiled and therefore runs slower than when running multiple times. A benchmark should always run the same test multiple times and ignore the first run(s). This benchmark runs three times, but only the last run is measured.

#### Memory Usage

It is not enough to measure the time taken, the memory usage is important as well. Performance can be improved by using a bigger cache, but the amount of memory is limited. HSQLDB tables are kept fully in memory by default; this benchmark uses 'disk based' tables for all databases. Unfortunately, it is not so easy to calculate the memory usage of PostgreSQL and MySQL, because they run in a different process than the test. This benchmark currently does not print memory usage of those databases.

#### Delayed Operations

Some databases delay some operations (for example flushing the buffers) until after the benchmark is run. This benchmark waits between each database tested, and each database runs in a different process (sequentially).

#### Transaction Commit / Durability

Durability means transaction committed to the database will not be lost. Some databases (for example MySQL) try to enforce this by default by calling `fsync()` to flush the buffers, but most hard drives don't actually flush all data. Calling the method slows down transaction commit a lot, but doesn't always make data durable. When comparing the results, it is important to think about the effect. Many database suggest to 'batch' operations when possible. This benchmark switches off autocommit when loading the data, and calls commit after each 1000 inserts. However many applications need 'short' transactions at runtime (a commit after each update). This benchmark commits after each update / delete in the simple benchmark, and after each business transaction in the other benchmarks. For databases that support delayed commits, a delay of one second is used.

#### Using Prepared Statements

Wherever possible, the test cases use prepared statements.

#### Currently Not Tested: Startup Time

The startup time of a database engine is important as well for embedded use. This time is not measured currently. Also, not tested is the time used to create a database and open an existing database. Here, one (wrapper) connection is opened at the start, and for each step a new connection is opened and then closed.

## PolePosition Benchmark

The PolePosition is an open source benchmark. The algorithms are all quite simple. It was developed / sponsored by db4o.

Test Case | Unit | H2 | HSQLDB | MySQL
--- | --- | --- | --- | ---
Melbourne write | ms | 369 | 249 | 2022
Melbourne read | ms | 47 | 49 | 93
Melbourne read_hot | ms | 24 | 43 | 95
Melbourne delete | ms | 147 | 133 | 176
Sepang write | ms | 965 | 1201 | 3213
Sepang read | ms | 765 | 948 | 3455
Sepang read_hot | ms | 789 | 859 | 3563
Sepang delete | ms | 1384 | 1596 | 6214
Bahrain write | ms | 1186 | 1387 | 6904
Bahrain query_indexed_string | ms | 336 | 170 | 693
Bahrain query_string | ms | 18064 | 39703 | 41243
Bahrain query_indexed_int | ms | 104 | 134 | 678
Bahrain update | ms | 191 | 87 | 159
Bahrain delete | ms | 1215 | 729 | 6812
Imola retrieve | ms | 198 | 194 | 4036
Barcelona write | ms | 413 | 832 | 3191
Barcelona read | ms | 119 | 160 | 1177
Barcelona query | ms | 20 | 5169 | 101
Barcelona delete | ms | 388 | 319 | 3287
Total | ms | 26724 | 53962 | 87112

There are a few problems with the PolePosition test:

- HSQLDB uses in-memory tables by default while H2 uses persistent tables. The HSQLDB version included in PolePosition does not support changing this, so you need to replace `poleposition-0.20/lib/hsqldb.jar` with a newer version (for example `hsqldb-1.8.0.7.jar`), and then use the setting `hsqldb.connecturl=jdbc:hsqldb:file:data/hsqldb/dbbench2;hsqldb.default_table_type=cached;sql.enforce_size=true` in the file `Jdbc.properties`.
- HSQLDB keeps the database open between tests, while H2 closes the database (losing all the cache). To change that, use the database URL `jdbc:h2:file:data/h2/dbbench;DB_CLOSE_DELAY=-1`
- The amount of cache memory is quite important, specially for the PolePosition test. Unfortunately, the PolePosition test does not take this into account.

## Application Profiling

### Analyze First

Before trying to optimize performance, it is important to understand where the problem is (what part of the application is slow). Blind optimization or optimization based on guesses should be avoided, because usually it is not an efficient strategy. There are various ways to analyze an application. Sometimes two implementations can be compared using `System.currentTimeMillis()`. But this does not work for complex applications with many modules, and for memory problems.

A simple way to profile an application is to use the built-in profiling tool of java. Example:

    java -Xrunhprof:cpu=samples,depth=16 com.acme.Test

Unfortunately, it is only possible to profile the application from start to end. Another solution is to create a number of full thread dumps. To do that, first run `jps -l` to get the process id, and then run `jstack <pid>` or `kill -QUIT <pid>` (Linux) or press Ctrl+C (Windows).

A simple profiling tool is included in H2. To use it, the application needs to be changed slightly. Example:

    import org.h2.util;
    ...
    profiler = new Profiler();
    profiler.startCollecting();
    // application code
    profiler.stopCollecting();
    System.out.println(profiler.getTop(3));

The profiler is built into the H2 Console tool, to analyze databases that open slowly. To use it, run the H2 Console, and then click on 'Test Connection'. Afterwards, click on "Test successful" and you get the most common stack traces, which helps to find out why it took so long to connect. You will only get the stack traces if opening the database took more than a few seconds.

## Database Profiling

The `ConvertTraceFile` tool generates SQL statement statistics at the end of the SQL script file. The format used is similar to the profiling data generated when using `java -Xrunhprof`. As an example, execute the the following script using the H2 Console:

    SET TRACE_LEVEL_FILE 3;
    DROP TABLE IF EXISTS TEST;
    CREATE TABLE TEST(ID INT PRIMARY KEY, NAME VARCHAR(255));
    @LOOP 1000 INSERT INTO TEST VALUES(?, ?);
    SET TRACE_LEVEL_FILE 0;

Now convert the `.trace.db` file using the `ConvertTraceFile` tool:

    java -cp h2*.jar org.h2.tools.ConvertTraceFile
        -traceFile "~/test.trace.db" -script "~/test.sql"

The generated file `test.sql` will contain the SQL statements as well as the following profiling data (results vary):

    -----------------------------------------
    -- SQL Statement Statistics
    -- time: total time in milliseconds (accumulated)
    -- count: how many times the statement ran
    -- result: total update count or row count
    -----------------------------------------
    -- self accu    time   count  result sql
    --  62%  62%     158    1000    1000 INSERT INTO TEST VALUES(?, ?);
    --  37% 100%      93       1       0 CREATE TABLE TEST(ID INT PRIMARY KEY, NAME VARCHAR(255));
    --   0% 100%       0       1       0 DROP TABLE IF EXISTS TEST;
    --   0% 100%       0       1       0 SET TRACE_LEVEL_FILE 3;

## Database Performance Tuning

### Keep Connections Open or Use a Connection Pool

If your application opens and closes connections a lot (for example, for each request), you should consider using a connection pool. Opening a connection using `DriverManager.getConnection` is specially slow if the database is closed. By default the database is closed if the last connection is closed.

If you open and close connections a lot but don't want to use a connection pool, consider keeping a 'sentinel' connection open for as long as the application runs, or use delayed database closing. See also Closing a database.

### Use a Modern JVM

Newer JVMs are faster. Upgrading to the latest version of your JVM can provide a "free" boost to performance. Switching from the default Client JVM to the Server JVM using the `-server` command-line option improves performance at the cost of a slight increase in start-up time.

### Virus Scanners

Some virus scanners scan files every time they are accessed. It is very important for performance that database files are not scanned for viruses. The database engine never interprets the data stored in the files as programs, that means even if somebody would store a virus in a database file, this would be harmless (when the virus does not run, it cannot spread). Some virus scanners allow to exclude files by suffix. Ensure files ending with `.db` are not scanned.

### Using the Trace Options

If the performance hot spots are in the database engine, in many cases the performance can be optimized by creating additional indexes, or changing the schema. Sometimes the application does not directly generate the SQL statements, for example if an O/R mapping tool is used. To view the SQL statements and JDBC API calls, you can use the trace options. For more information, see Using the Trace Options.

### Index Usage

This database uses indexes to improve the performance of `SELECT, UPDATE, DELETE`. If a column is used in the `WHERE` clause of a query, and if an index exists on this column, then the index can be used. Multi-column indexes are used if all or the first columns of the index are used. Both equality lookup and range scans are supported. Indexes are used to order result sets, but only if the condition uses the same index or no index at all. The results are sorted in memory if required. Indexes are created automatically for primary key and unique constraints. Indexes are also created for foreign key constraints, if required. For other columns, indexes need to be created manually using the `CREATE INDEX` statement.

### Optimizer

This database uses a cost based optimizer. For simple and queries and queries with medium complexity (less than 7 tables in the join), the expected cost (running time) of all possible plans is calculated, and the plan with the lowest cost is used. For more complex queries, the algorithm first tries all possible combinations for the first few tables, and the remaining tables added using a greedy algorithm (this works well for most joins). Afterwards a genetic algorithm is used to test at most 2000 distinct plans. Only left-deep plans are evaluated.

### Expression Optimization

After the statement is parsed, all expressions are simplified automatically if possible. Operations are evaluated only once if all parameters are constant. Functions are also optimized, but only if the function is constant (always returns the same result for the same parameter values). If the `WHERE` clause is always false, then the table is not accessed at all.

### COUNT(*) Optimization

If the query only counts all rows of a table, then the data is not accessed. However, this is only possible if no `WHERE` clause is used, that means it only works for queries of the form `SELECT COUNT(*) FROM table`.

### Updating Optimizer Statistics / Column Selectivity

When executing a query, at most one index per joined table can be used. If the same table is joined multiple times, for each join only one index is used. Example: for the query `SELECT * FROM TEST T1, TEST T2 WHERE T1.NAME='A' AND T2.ID=T1.ID`, two index can be used, in this case the index on NAME for T1 and the index on ID for T2.

If a table has multiple indexes, sometimes more than one index could be used. Example: if there is a table `TEST(ID, NAME, FIRSTNAME)` and an index on each column, then two indexes could be used for the query `SELECT * FROM TEST WHERE NAME='A' AND FIRSTNAME='B'`, the index on NAME or the index on FIRSTNAME. It is not possible to use both indexes at the same time. Which index is used depends on the selectivity of the column. The selectivity describes the 'uniqueness' of values in a column. A selectivity of 100 means each value appears only once, and a selectivity of 1 means the same value appears in many or most rows. For the query above, the index on NAME should be used if the table contains more distinct names than first names.

The SQL statement `ANALYZE` can be used to automatically estimate the selectivity of the columns in the tables. This command should be run from time to time to improve the query plans generated by the optimizer.

### In-Memory (Hash) Indexes

Using in-memory indexes, specially in-memory hash indexes, can speed up queries and data manipulation.

In-memory indexes are automatically used for in-memory databases, but can also be created for persistent databases using `CREATE MEMORY TABLE`. In many cases, the rows itself will also be kept in-memory. Please note this may cause memory problems for large tables.

In-memory hash indexes are backed by a hash table and are usually faster than regular indexes. However, hash indexes only supports direct lookup (`WHERE ID = ?`) but not range scan (`WHERE ID < ?`). To use hash indexes, use HASH as in: `CREATE UNIQUE HASH INDEX` and `CREATE TABLE ...(ID INT PRIMARY KEY HASH,...)`.

### Use Prepared Statements

If possible, use prepared statements with parameters.

### Prepared Statements and IN(...)

Avoid generating SQL statements with a variable size IN(...) list. Instead, use a prepared statement with arrays as in the following example:

    PreparedStatement prep = conn.prepareStatement(
        "SELECT * FROM TABLE(X INT=?) T INNER JOIN TEST ON T.X=TEST.ID");
    prep.setObject(1, new Object[] { "1", "2" });
    ResultSet rs = prep.executeQuery();

### Optimization Examples

See `src/test/org/h2/samples/optimizations.sql` for a few examples of queries that benefit from special optimizations built into the database.

### Cache Size and Type

By default the cache size of H2 is quite small. Consider using a larger cache size, or enable the second level soft reference cache. See also Cache Settings.

### Data Types

Each data type has different storage and performance characteristics:

- The `DECIMAL/NUMERIC` type is slower and requires more storage than the `REAL` and `DOUBLE` types.
- Text types are slower to read, write, and compare than numeric types and generally require more storage.
- See Large Objects for information on `BINARY` vs. `BLOB` and `VARCHAR` vs. `CLOB` performance.
- Parsing and formatting takes longer for the `TIME`, `DATE`, and `TIMESTAMP` types than the numeric types.
- `SMALLINT/TINYINT/BOOLEAN` are not significantly smaller or faster to work with than `INTEGER` in most modes.

### Sorted Insert Optimization

To reduce disk space usage and speed up table creation, an optimization for sorted inserts is available. When used, b-tree pages are split at the insertion point. To use this optimization, add `SORTED` before the `SELECT` statement:

    CREATE TABLE TEST(ID INT PRIMARY KEY, NAME VARCHAR) AS
        SORTED SELECT X, SPACE(100) FROM SYSTEM_RANGE(1, 100);
    INSERT INTO TEST
        SORTED SELECT X, SPACE(100) FROM SYSTEM_RANGE(101, 200);

## Using the Built-In Profiler

A very simple Java profiler is built-in. To use it, use the following template:

    import org.h2.util.Profiler;
    Profiler prof = new Profiler();
    prof.startCollecting();
    // .... some long running process, at least a few seconds
    prof.stopCollecting();
    System.out.println(prof.getTop(3));

## Fast Database Import

To speed up large imports, consider using the following options temporarily:

- `SET CACHE_SIZE` (a large cache is faster)
- `SET LOCK_MODE 0` (disable locking)
- `SET UNDO_LOG 0` (disable the session undo log)

These options can be set in the database URL: `jdbc:h2:~/test;CACHE_SIZE=65536;LOCK_MODE=0;UNDO_LOG=0`. Most of those options are not recommended for regular use, that means you need to reset them after use.