The Ultimate Guide to High Availability with MariaDB

S3 storage engine internals

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Contents

  1. ALTER TABLE
  2. Big reads
  3. Compression
  4. Structure stored on S3
  5. Using the awsctl python tool to examine data
    1. Installing awsctl on Linux
    2. Using the awsctl tool
  6. See also

S3 storage engine internals

The S3 storage engine is based on the Aria code. Internally the S3 storage inherits from the Aria code, with hooks that changes reads so that instead of reading data from the local disk it reads things from S3.

The S3 engine uses it's own page cache, modified to be able to handle reading of blocks from S3 (of size s3_block_size). Internally the S3 page cache uses pages of aria-block-size for splitting the blocks read from S3.

ALTER TABLE

The ALTER TABLE will first create a local table in the normal Aria on disk format and then move both index and data to S3 in buckets of S3_BLOCK_SIZE. The .frm file is also copied to S3 for discovery to support discovery for other MariaDB servers.

Big reads

One of the properties of many S3 implementations is that it favors big reads. It's said that 4M gives the best performance, which is why the default value for S3_BLOCK_SIZE is 4M.

Compression

If compression (COMPRESSION_ALGORITHM=zlib) is used then all index blocks and data blocks are compressed. The .frm file and Aria definition header (first page/pages in the index file) are not compresseded as these are used by discovery/open.

If compression is used, then locial block size is S3_BLOCK_SIZE, but but the block stored in S3# will be the size of the compressed block.

Structure stored on S3

The table will be copied in S3 into the following locations:

frm file (for discovery):
s3_bucket/database/table/frm

First index block (contains description if the Aria file):
s3_bucket/database/table/aria

Rest of the index file:
s3_bucket/database/table/index/block_number

Data file:
aws_bucket/database/table/data/block_number

block_number is 6 digits decimal number, prefixed with 0 (Can be larger than 6 numbers, the prefix is just for nice output)

Using the awsctl python tool to examine data

Installing awsctl on Linux

#install python-pip
zypper install python-pip# install aws client
pip install --upgrade pip

# The following installs awscli tools in ~/.local/bin
pip install --upgrade --user awscli
export PATH=~/.local/bin:$PATH

# configure your aws credentials
aws configure

Using the awsctl tool

One can use the aws python tool to see how things are stored on S3:

shell> aws s3 ls --recursive s3://mariadb-bucket/
2019-05-10 17:46:48       8192 foo/test1/aria
2019-05-10 17:46:49    3227648 foo/test1/data/000001
2019-05-10 17:46:48        942 foo/test1/frm
2019-05-10 17:46:48    1015808 foo/test1/index/000001

To delete an obsolete table foo.test1 one can do:

shell> ~/.local/bin/aws s3 rm --recursive s3://mariadb-bucket/foo/test1
delete: s3://mariadb-bucket/foo/test1/aria
delete: s3://mariadb-bucket/foo/test1/data/000001
delete: s3://mariadb-bucket/foo/test1/frm
delete: s3://mariadb-bucket/foo/test1/index/000001

See also

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