- Aerospike
- Akamas
- AlloyDB
- ApertureDB
- Arrow
- Berkeley DB
- BlazingDB
- Brytlyt
- Chaos Mesh
- Citus
- CockroachDB
- Convex
- CrateDB
- Databricks
- Datometry
- dbt
- Delta Lake
- Dremio
- DSQL
- DVMS
- EraDB
- eXtremeDB
- Fauna
- Featureform
- Firebolt
- Fluss
- Gaia
- GlareDB
- GoogleSQL
- GreptimeDB
- Heron
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Milvus
- MonetDB
- Mooncake
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- OxQL
- Pinecone
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- SingleStore
- SLOG
- Snowflake
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TiDB
- TileDB
- Tokutek
- Umbra
- Vertica
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- AirFlow
- Alibaba
- Anna
- APOLLO
- Azure Cosmos DB
- BigQuery
- Bodo
- Cassandra
- Chroma
- ClickHouse
- Confluent
- CouchDB
- CrocodileDB
- DataFusion
- Datomic
- Debezium
- Dolt
- Druid
- DuckDB
- EdgeDB
- Exon
- FASTER
- FeatureBase
- Feldera
- Fluree
- FoundationDB
- Gel
- Google Spanner
- Greenplum
- HarperDB
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Modin
- MongoDB
- MotherDuck
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- ParadeDB
- Pinot
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- sled
- Smooth
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TerminusDB
- TigerBeetle
- TimescaleDB
- Trino
- Velox
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- Aerospike
- Alibaba
- ApertureDB
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Citus
- Confluent
- CrateDB
- DataFusion
- dbt
- Dolt
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Milvus
- MongoDB
- MySQL
- NoisePage
- OceanBase
- OtterTune
- Pinecone
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- SLOG
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TigerBeetle
- Tokutek
- Velox
- VoltDB
- WiredTiger
- YugabyteDB
- AirFlow
- AlloyDB
- APOLLO
- Berkeley DB
- Bodo
- Chaos Mesh
- ClickHouse
- Convex
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Modin
- Mooncake
- Napa
- Noria
- OpenDAL
- OxQL
- Pinot
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- SingleStore
- Smooth
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TerminusDB
- TileDB
- Trino
- Vertica
- Vortex
- XTDB
- Akamas
- Anna
- Arrow
- BigQuery
- Brytlyt
- Chroma
- CockroachDB
- CouchDB
- Databricks
- Datomic
- Delta Lake
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- MonetDB
- MotherDuck
- Neon
- NuoDB
- Oracle
- ParadeDB
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- sled
- Snowflake
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TiDB
- TimescaleDB
- Umbra
- Vitesse
- Weaviate
- Yellowbrick
- Aerospike
- AlloyDB
- Arrow
- BlazingDB
- Chaos Mesh
- CockroachDB
- CrateDB
- Datometry
- Delta Lake
- DSQL
- EraDB
- Fauna
- Firebolt
- Gaia
- GoogleSQL
- Heron
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- MonetDB
- MySQL
- Noria
- Oracle
- Pinecone
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- SLOG
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TiDB
- Tokutek
- Vertica
- Weaviate
- YugabyteDB
- AirFlow
- Anna
- Azure Cosmos DB
- Bodo
- Chroma
- Confluent
- CrocodileDB
- Datomic
- Dolt
- DuckDB
- Exon
- FeatureBase
- Fluree
- Gel
- Greenplum
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MongoDB
- Napa
- NuoDB
- OtterTune
- Pinot
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- Smooth
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TigerBeetle
- Trino
- Vitesse
- WiredTiger
- Akamas
- ApertureDB
- Berkeley DB
- Brytlyt
- Citus
- Convex
- Databricks
- dbt
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluss
- GlareDB
- GreptimeDB
- Iceberg
- kdb
- LeanStore
- MapD
- Milvus
- Mooncake
- Neon
- OceanBase
- OxQL
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- SingleStore
- Snowflake
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TileDB
- Umbra
- VoltDB
- XTDB
- Alibaba
- APOLLO
- BigQuery
- Cassandra
- ClickHouse
- CouchDB
- DataFusion
- Debezium
- Druid
- EdgeDB
- FASTER
- Feldera
- FoundationDB
- Google Spanner
- HarperDB
- Impala
- Kinetica
- Litestream
- MariaDB
- Modin
- MotherDuck
- NoisePage
- OpenDAL
- ParadeDB
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- sled
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TerminusDB
- TimescaleDB
- Velox
- Vortex
- Yellowbrick
Sep 13
2019
Fall 2019: Pat Helland (SalesForce)
- Speaker:
- Pat Helland
- System:
- SalesForce
This talk is a summary of my soon to be released column in ACM Queue titled "Write Amplification versus Read Perspiration". In this short discussion, we observe that there is a strong pattern in which writing data incurs and obligation to do more work to make it easy to read that data later. We frequently talk about write amplification to... Read More
Sep 13
2019
Fall 2019: Rohit Agrawal (SalesForce)
- Speaker:
- Rohit Agrawal
- System:
- SalesForce
In this talk we discuss LSM compression for a KV store. In our KV store, we write to an underlying shared storage system that models data as named extents (up to 2GB) and variable-length fragments contained within the extent. Fragments are max of 1MB and are the atomic unit of read and write. Our KV store reads fragments into 64K... Read More
Sep 9
2019
[DB Seminar] Fall 2019 DB Group: Alex Smola (Amazon)
- Speaker:
- Alex Smola
In this talk I will give a sample of some of the research done at AWS. In particular I will talk about some recent results in Reinforcement Learning using a combined on-policy and off-policy approach to obtain rapidly converging and sample efficient algorithms. The key idea in this work is to use propensity scoring and effective sample size reweighting to... Read More
Aug 12
2019
[DB Seminar] Summer 2019 DB Group: Andy Pavlo
- Speaker:
- Andy Pavlo
The current research trend is on developing "learned" components to supplement and replace legacy components in database management systems (DBMSs). Such learned components use machine learning (ML) methods to identify non-trivial trends and correlations in the DBMS's runtime behavior. They then use this information to create execution strategies and data structures that are tailored to the application's access patterns. The... Read More
Jul 29
2019
[DB Seminar] Summer 2019 DB Group: Chenyao Lou
- Speaker:
- Chenyao Lou
Title: NEVER use mmap for your database Abstract: MMAP can be used as the buffer pool manager for DBMSs. But is it good to use mmap for DBMSs? Chenyao is going to share evaluations for mmap, pitfalls in mmap, and methods to make mmap safe in existing DBMSs. Read More
Jul 25
2019
[DB Seminar] Summer 2019: Lucas Lersch (TU Dresden)
- Speaker:
- Lucas Lersch
Non-volatile memory technologies (NVM) enable persistent media to be directly accessed by the CPU through its caches. The biggest challenge introduced by NVM is the little control the application has when persisting data. This stems from the fact that it is not possible to prevent data from being evicted from the CPU cache to NVM at arbitrary points in time,... Read More
Jul 22
2019
[DB Seminar] Summer 2019 DB Group: Amadou Ngom
- Speaker:
- Amadou Ngom
Amadou will present this paper in this meeting: Title: SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning Authors: Immanuel Trummer, Junxiong Wang, Deepak Maram, Samuel Moseley, Saehan Jo, Joseph Antonakakis Read More
Jul 15
2019
[DB Seminar] Summer 2019 DB Group: Vivian Huang
- Speaker:
- Vivian Huang
Vivian will present this paper in this meeting: Title: Plan Stitch: Harnessing the Best of Many Plans Authors: Bailu Ding, Sudipto Das, Wentao Wu, Surajit Chaudhuri, Vivek Narasayya Read More
Jul 8
2019
[DB Seminar] Summer 2019 DB Group: Perf Tutorial
In this DB group meeting, we are going to watch this perf tutorial together: https://www.youtube.com/watch?v=nXaxk27zwlk Read More
Jun 24
2019
[DB Seminar] Summer 2019 DB Group: Yangjun Sheng
- Speaker:
- Yangjun Sheng
Yangjun will give a practice talk for the SIGMOD/AiDM workshop. Title: Scheduling OLTP Transactions via Learned Abort Prediction Abstract: Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase. These systems schedule transactions randomly across cores to maximize concurrency and... Read More