SlateDB – An embedded storage engine built on object storage
SlateDB is an embedded storage engine built as a log-structured merge-tree. Unlike traditional LSM-tree storage engines, SlateDB writes data to object storage (S3, GCS, ABS, MinIO, Tigris, and so on). Leveraging object storage allows SlateDB to provide bottomless storage capacity, high durability, and easy replication. The trade-off is that object storage has a higher latency and higher API cost than local disk. To mitigate high write API costs (PUTs), SlateDB batches writes. Rather than writing every put() call to object storage, MemTables are flushed periodically to object storage as a…
In plain words
SlateDB is an embedded storage engine that uses object storage services like S3 and GCS instead of local disk. Built as a log-structured merge-tree, it offers unlimited storage capacity, high durability, and simple replication across systems. To overcome object storage's higher latency and API costs, SlateDB batches writes into periodic flushes and provides an async put method, letting developers choose between strong durability and lower latency. It's designed for applications needing scalable embedded storage without traditional disk constraints.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
SlateDB is an embedded storage engine built as a log-structured merge-tree. Unlike traditional LSM-tree storage engines, SlateDB writes data to object storage (S3, GCS, ABS, MinIO, Tigris, and so on). Leveraging object storage allows SlateDB to provide bottomless storage capacity, high durability, and easy replication. The trade-off is that object storage has a higher latency and higher API cost than local disk. To mitigate high write API costs (PUTs), SlateDB batches writes. Rather than writing every put() call to object storage, MemTables are flushed periodically to object storage as a string-sorted table (SST). The flush interval is configurable. To mitigate write latency, SlateDB provides an async put method. Clients that prefer strong durability can await on put until the MemTable is flushed to object storage (trading latency for durability). Clients that prefer lower latency can simply ignore the future returned by put. To mitigate read latency and read API costs (GETs), SlateDB will use standard LSM-tree caching techniques: in-memory block caches, compression, bloom filters, and local SST disk caches.
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