mi

Extension for sqlite that provides transparent dictionary-based row-level compression for sqlite. This basically allows you to compress entries in a sqlite database almost as well as if you were compressing the whole DB file, but while retaining random access.

Depending on the data, this can reduce the size of the database by 90% while keeping performance mostly the same.

Transparent Compression

• zstd_enable_transparent(config)

  Enable transparent row-level compression of the given column on the given table.

  The data will be moved to _table_name_zstd, while table_name will be a view that can be queried as normally, including SELECT, INSERT, UPDATE, and DELETE queries. This function will not compress any data by itself, you need to call zstd_incremental_maintenance afterwards.

  config is a json object describing the configuration. See TransparentCompressConfig for detail.

  The following differences apply when compression is active:

  • The compressed column may only contain blob or text data, depending on the affinity of the declared data type (e.g. VARCHAR(10) is fine, but int is not).
  • The primary key must not be null for any row, otherwise updating may not work as expected
  • sqlite3_changes() will return 0 for modifying queries (see here).
  • The SQLite streaming blob reading API will be somewhat useless since the blob is fully copied into memory anyways.
  • Attaching a database containing compressed tables using ATTACH 'foo.db' is not supported.

• zstd_incremental_maintenance(duration_seconds: float, db_load: float)

  Perform an incremental maintenance operation taking around the given amount of time. This will train dictionaries and compress data based on the grouping given in the TransparentCompressConfig. db_load specifies the ratio of time the db will be locked with write queries. For example: if set to 0.5, after each write operation taking 2 seconds the maintenance function will sleep for 2 seconds so other processes have time to run write operations against the database. If set to 1, the maintenance will not sleep. Note that this is only useful if you run the incremental maintenance function in a separate thread or process than your other logic.

  Returns 1 if there is more work to be done, 0 if everything is compressed as it should.

  Note that each call of this function has a start up time cost equivalent to select * from table where dictid is null, so longer durations are more efficient.

  This function can safely be interrupted at any time, each chunk of compression work is done as an atomic operation.

  Example output:

  sqlite> select zstd_incremental_maintenance(60);
    [2020-12-23T21:11:31Z WARN  mi::transparent] Warning: It is recommended to set `pragma busy_timeout=2000;` or higher
    [2020-12-23T21:11:40Z INFO  mi::transparent] events.data: Total 5.20GB to potentially compress.
    3[2020-12-23T21:13:22Z INFO  mi::transparent] Compressed 6730 rows with dictid=109. Total size of entries before: 163.77MB, afterwards: 2.12MB, (average: before=24.33kB, after=315B)
    [2020-12-23T21:13:43Z INFO  mi::transparent] Compressed 4505 rows with dictid=110. Total size of entries before: 69.28MB, afterwards: 1.60MB, (average: before=15.38kB, after=355B)
    [2020-12-23T21:14:06Z INFO  mi::transparent] Compressed 5228 rows with dictid=111. Total size of entries before: 91.97MB, afterwards: 1.41MB, (average: before=17.59kB, after=268B)
  

Basic Functionality

• zstd_compress(data: text|blob, level: int = 3, dictionary: blob | int | null = null, compact: bool = false) -> blob

  Compresses the given data, with the compression level (1 - 22, default 3)

  • If dictionary is a blob it will be directly used
  • If dictionary is an int i, it is functionally equivalent to zstd_compress(data, level, (select dict from _zstd_dict where id = i))
  • If dictionary is not present, null, or -1, the data is compressed without a dictionary.

  if compact is true, the output will be without magic header, without checksums, and without dictids. This will save 4 bytes when not using dictionaries and 8 bytes when using dictionaries. The same compact argument must also be passed to the decompress function.

• zstd_decompress(data: blob, is_text: bool, dictionary: blob | int | null = null, compact: bool = false) -> text|blob

  Decompresses the given data. if the dictionary is wrong, the result is undefined

  • If dictionary is a blob it will be directly used
  • If dictionary is an int i, it is functionally equivalent to zstd_decompress(data, (select dict from _zstd_dict where id = i)).
  • If dictionary is not present, null, or -1, it is assumed the data was compressed without a dictionary.

  Note that passing dictionary as an int is recommended, since then the dictionary only has to be prepared once.

  is_text specifies whether to output the data as text or as a blob. Note that when outputting as text the encoding depends on the sqlite database encoding. mi is only tested with UTF-8.

  compact must be specified when the compress function was also called with compact.

• zstd_train_dict(agg, dict_size: int, sample_count: int) -> blob

  Aggregate function (like sum() or count()) to train a zstd dictionary on sample_count samples of the given aggregate data

  Example use: select zstd_train_dict(tbl.data, 100000, 1000) from tbl will return a dictionary of size 100kB trained on 1000 samples in tbl

  The recommended number of samples is 100x the target dictionary size. As an example, you can train a dict of 100kB with the "optimal" sample count as follows:

  select zstd_train_dict(data, 100000, (select (100000 * 100 / avg(length(data))) as sample_count from tbl))
                  as dict from tbl
  

  Note that dict_size and sample_count are assumed to be constants.

• zstd_train_dict_and_save(agg, dict_size: int, sample_count: int) -> int

  Same as zstd_train_dict, but the dictionary is saved to the _zstd_dicts table and the id is returned.

Installation

You can either load this library as SQLite extension or as a Rust library. Note that sqlite extensions are not persistent, so you need to load it each time you connect to the database.

Sqlite CLI

Either load it in the REPL:

$ sqlite3 file.db
SQLite version 3.34.0 2020-12-01 16:14:00
sqlite> .load .../libmi.so
[2020-12-23T21:30:02Z INFO  mi::create_extension] [mi] initialized
sqlite>

Or alternatively:

sqlite3 -cmd '.load libmi.so'

C Api

int success = sqlite3_load_extension(db, "libmi.so", NULL, NULL);

See here for more information.

Rust

The recommended method is to add mi as a dependency to your project, then load it using

let conn: rusqlite::Connection;
mi::load(&conn)?;

Alternatively, you can load the extension like any other extension:

let conn: rusqlite::Connection;
conn.load_extension("libmi.so", None)?;

See here for more information.

Verbosity / Debugging

You can change the log level by setting the environment variable RUST_LOG=mi=error for less logging and RUST_LOG=mi=debug for more logging.

Future Work / Ideas / Todo

TODO: describe

• fix multiple open connections regarding global dict cache
• data use statistics
• performance implications
• how it works
• investigate startup cost without dictionary
• correctly handle indices over compressed columns
• do compression in different thread(s) for performance (e.g. using .multithread(1) in zstd?)
• type affinity interfers with int pass through - insert into compressed (col) values (1) will result in typeof(col) = text instead of integer if the type of the column was declared as text - which in turns causes decompression to fail with "got string, but zstd compressed data is always blob"
  • either change the type of the compressed column to blob or similar or disallow integer passthrough

select zstd_enable_transparent('{"table": "events", "column": "data", "compression_level": 19, "dict_chooser": "case when date(timestamp, ''weekday 0'') < date(''now'', ''weekday 0'') then data_type || ''.'' || date(timestamp, ''weekday 0'') else null end"}');

select case when date(timestamp, 'weekday 0') < date('now', 'weekday 0') then data_type || '.' || date(timestamp, 'weekday 0') else null END from events limit 10000