- Aerospike
- Akamas
- AlloyDB
- ApertureDB
- Arrow
- Azure Cosmos DB
- BigQuery
- Bodo
- Cassandra
- Chroma
- Citus
- CockroachDB
- Convex
- CrateDB
- Databricks
- Datometry
- dbt
- Delta Lake
- Dremio
- DSQL
- DVMS
- EraDB
- eXtremeDB
- Fauna
- Featureform
- Firebolt
- Fluree
- FoundationDB
- Gel
- Google Spanner
- Greenplum
- HarperDB
- HorizonDB
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Microsoft SQL Server
- Modin
- MongoDB
- MotherDuck
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- Oxla
- ParadeDB
- Pinot
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- SingleStore
- sled
- Smooth
- SpacetimeDB
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TiDB
- TileDB
- Tokutek
- TopK
- turbopuffer
- Velox
- VillageSQL
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- AirFlow
- Alibaba
- Anna
- APOLLO
- Aurora DSQL
- Berkeley DB
- BlazingDB
- Brytlyt
- Chaos Mesh
- Chronon
- ClickHouse
- Confluent
- CouchDB
- CrocodileDB
- DataFusion
- Datomic
- Debezium
- Dolt
- Druid
- DuckDB
- EdgeDB
- Exon
- FASTER
- FeatureBase
- Feldera
- Floe
- Fluss
- Gaia
- GlareDB
- GoogleSQL
- GreptimeDB
- Heron
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Milvus
- MonetDB
- Mooncake
- Multigres
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- OxQL
- Pinecone
- Pixeltable
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- Sirius
- SLOG
- Snowflake
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TerminusDB
- TigerBeetle
- TimescaleDB
- TonicDB
- Trino
- Umbra
- Vertica
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- Aerospike
- Alibaba
- ApertureDB
- Aurora DSQL
- BigQuery
- Brytlyt
- Chroma
- ClickHouse
- Convex
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Floe
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- Modin
- Mooncake
- MySQL
- NoisePage
- OceanBase
- OtterTune
- ParadeDB
- Pixeltable
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- SingleStore
- SLOG
- SpacetimeDB
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TiDB
- TimescaleDB
- TopK
- Umbra
- VillageSQL
- Vortex
- XTDB
- AirFlow
- AlloyDB
- APOLLO
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Chronon
- CockroachDB
- CouchDB
- Databricks
- Datomic
- Delta Lake
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- HorizonDB
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Microsoft SQL Server
- MonetDB
- MotherDuck
- Napa
- Noria
- OpenDAL
- Oxla
- Pinecone
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- Sirius
- Smooth
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TigerBeetle
- Tokutek
- Trino
- Velox
- Vitesse
- Weaviate
- Yellowbrick
- Akamas
- Anna
- Arrow
- Berkeley DB
- Bodo
- Chaos Mesh
- Citus
- Confluent
- CrateDB
- DataFusion
- dbt
- Dolt
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Milvus
- MongoDB
- Multigres
- Neon
- NuoDB
- Oracle
- OxQL
- Pinot
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- sled
- Snowflake
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TerminusDB
- TileDB
- TonicDB
- turbopuffer
- Vertica
- VoltDB
- WiredTiger
- YugabyteDB
- Aerospike
- AlloyDB
- Arrow
- BigQuery
- Cassandra
- Citus
- Convex
- Databricks
- dbt
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluree
- Gel
- Greenplum
- HorizonDB
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- Modin
- MotherDuck
- Neon
- OceanBase
- Oxla
- Pinot
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- sled
- SpacetimeDB
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TileDB
- TopK
- Velox
- VoltDB
- XTDB
- AirFlow
- Anna
- Aurora DSQL
- BlazingDB
- Chaos Mesh
- ClickHouse
- CouchDB
- DataFusion
- Debezium
- Druid
- EdgeDB
- FASTER
- Feldera
- Fluss
- GlareDB
- GreptimeDB
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MonetDB
- Multigres
- NoisePage
- OpenDAL
- OxQL
- Pixeltable
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- SLOG
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TerminusDB
- TimescaleDB
- Trino
- Vertica
- Vortex
- Yellowbrick
- Akamas
- ApertureDB
- Azure Cosmos DB
- Bodo
- Chroma
- CockroachDB
- CrateDB
- Datometry
- Delta Lake
- DSQL
- EraDB
- Fauna
- Firebolt
- FoundationDB
- Google Spanner
- HarperDB
- Iceberg
- kdb
- LeanStore
- MapD
- Microsoft SQL Server
- MongoDB
- MySQL
- Noria
- Oracle
- ParadeDB
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- SingleStore
- Smooth
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TiDB
- Tokutek
- turbopuffer
- VillageSQL
- Weaviate
- YugabyteDB
- Alibaba
- APOLLO
- Berkeley DB
- Brytlyt
- Chronon
- Confluent
- CrocodileDB
- Datomic
- Dolt
- DuckDB
- Exon
- FeatureBase
- Floe
- Gaia
- GoogleSQL
- Heron
- Impala
- Kinetica
- Litestream
- MariaDB
- Milvus
- Mooncake
- Napa
- NuoDB
- OtterTune
- Pinecone
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- Sirius
- Snowflake
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TigerBeetle
- TonicDB
- Umbra
- Vitesse
- WiredTiger
May 3
2016
[PDL Visit Day 2016] Siying Dong (Facebook)
- Speaker:
- Siying Dong
- System:
- RocksDB
RocksDB is an embedded persistent key-value store for low-latency and high-throughput workload. It has been adapted to a wide range of workloads, including RocksDB as an embedded DBMS and as storage engines of other DBMS systems. Our benchmarks show RocksDB can achieve 126K random reads per second on flash and 7 million random reads per second on memory. RocksDB also... Read More
Oct 22
2015
The Journey from Faster to Better (Igor Canadi + Mark Callaghan)
- Speakers:
- Igor Canadi , Mark Callaghan
- System:
- RocksDB
- Video:
- YouTube
RocksDB has been adapted to a wide range of workloads. Early adopters needed a DBMS for low-latency & high-throughput workloads. Using RocksDB as an embedded DBMS eliminates network latency per request. When using RocksDB with a fast SSD we are able to get more IO throughput compared to other open-source DBMS that we use in production. We continue to get... Read More