🌟Dolt 2.0 Released With Auto Storage Optimization
Auto storage optimization and vector support in Dolt 2.0
TL;DR
Dolt 2.0 introduces auto storage optimization with garbage collection and compression, supporting large and vector data types. The new 'archives' format reduces the storage footprint by up to 50% through dictionary compression.
Dolt 2.0 has arrived with automatic storage optimization, including garbage collection and compression features. This update is a big deal for developers working with large datasets or requiring version-controlled vector indexes. The new 'archives' format reduces the storage footprint by up to 50% through dictionary compression, making Dolt faster than MySQL on sysbench writes (13%) and reads (5%). With support for MariaDB's Vector type, Dolt is now the only database offering version-controlled vectors in beta.
Key Points
Dolt 2.0 includes automatic garbage collection and archive compression enabled by default, reducing storage footprint up to 50%
New 'archives' format uses dictionary compression for deduplicated storage
Supports MariaDB's Vector type for version-controlled vector indexes in beta
Faster than MySQL on sysbench writes (13%) and reads (5%)
Dolt provides a MySQL query interface called DoltgreSQL
Why It Matters
If you're working with large datasets or need version-controlled vectors, Dolt 2.0's auto storage optimization and new 'archives' format can reduce your storage footprint by up to 50%. This makes it faster than MySQL on sysbench writes (13%) and reads (5%). The beta support for vector indexes will be removed once read-path gaps are fixed.
Frequently Asked Questions
Why does this matter?
If you're working with large datasets or need version-controlled vectors, Dolt 2.0's auto storage optimization and new 'archives' format can reduce your storage footprint by up to 50%. This makes it faster than MySQL on sysbench writes (13%) and reads (5%). The beta support for vector indexes will be removed once read-path gaps are fixed.
What happened?
Dolt 2.0 introduces auto storage optimization with garbage collection and compression, supporting large and vector data types. The new 'archives' format reduces the storage footprint by up to 50% through dictionary compression.
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