- 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
Aug 3
2020
YugabyteDB: Bringing Together the Best of Amazon Aurora and Google Spanner
- Speaker:
- Karthik Ranganathan
- System:
- YugabyteDB
- Video:
- YouTube
PostgreSQL, a single-node open-source RDBMS, is widely adopted for its powerful set of features. However, PostgreSQL is not built to be used as a cloud-native database, and therefore cannot inherently survive failures, scale horizontally or support geo-distributed deployments. While Amazon Aurora has modified the subsystem of PostgreSQL that writes to disk along with simplifying async replication to make the database resilient... Read More
Jul 27
2020
Black-box Isolation Checking with Elle
- Speaker:
- Kyle Kingsbury
- System:
- Jepsen
- Video:
- YouTube
Databases are awful. They lose information, corrupt state, and do other terrible things, both by design and by accident. You'd think that *testing* databases to see how awful they are would help make them better, but it turns out that testing most of the useful database safety properties is *also* awful. We came up with a better way to test... Read More
Jul 24
2020
MS Thesis Defense: Filter Representation in Vectorized Query Execution (Amadou Ngom)
- Speaker:
- Amadou Ngom
Advances in memory capacity have allowed Database Management Systems (DBMSs) to store large amounts of data in memory, thereby shifting the performance bottleneck of query execution from disk accesses to CPU efficiency (i.e., instruction count and cycles per instruction). One technique used to achieve such efficiency in analytical applications is batch-oriented processing or vectorization: it reduces interpretation overhead, improves cache... Read More
Jul 20
2020
Rockset: Realtime Indexing for fast queries on massive semi-structured data
- Speaker:
- Dhruba Borthakur
- System:
- Rockset
- Video:
- YouTube
Rockset is a realtime indexing database that powers fast SQL over semi-structured data such as JSON, Parquet, or XML without requiring any schematization. All data loaded into Rockset are automatically indexed and a fully featured SQL engine powers fast queries over semi-structured data without requiring any database tuning. Rockset exploits the hardware fluidity available in the cloud and automatically grows... Read More
Jul 13
2020
Astra: How we built a Cassandra-as-a-Service
- Speakers:
- Jim McCollom , Jeff Carpenter
- System:
- Cassandra
- Video:
- YouTube
At DataStax, we’ve been on a multi-year journey to bring a Cassandra DBaaS to the market, culminating in the GA of Astra in May 2020. In this talk, we’ll share our successes and failures through the iterative journey to GA, our current Kubernetes based architecture, how we built scalability and reliability into the platform, and how Cassandra’s architecture and implementation... Read More
Jul 6
2020
Another Relational Database, Why and How
- Speaker:
- Oscar Batori & Zach Musgrave
- System:
- Dolt
- Video:
- YouTube
There are a lot of relational database, so a fair question is why we decided to create a new one. The primary reason is trade-offs. Relational database are optimized for storing a single version of the truth and providing it or updating it with maximum efficiency. More succinctly they are optimized for being good OLTP stores. They are not optimized... Read More
Jun 29
2020
[DB Seminar] Spring 2020 DB Group: Linux 4.x Tracing (Pre-Recorded)
- Speaker:
- Brendan Gregg
There is no invited speaker today. We will instead watch this video together: Linux 4.x Tracing: Performance Analysis with bcc/BPF (eBPF) Brendan Gregg https://youtu.be/w8nFRoFJ6EQ Zoom Password: 264771 Read More
Jun 22
2020
Testing Cloud-Native Databases with Chaos Mesh
- Speaker:
- Siddon Tang
- System:
- Chaos Mesh
- Video:
- YouTube
In the world of distributed computing, faults happen to clusters unpredictably, especially when they run in the cloud. To make a distributed database like TiDB resilient enough, chaos engineering is the way to go. At PingCAP, we use Chaos Mesh®, an open-source chaos engineering platform for Kubernetes to improve the resiliency of TiDB. Chaos Mesh adopts a cloud-native design and currently... Read More
Jun 15
2020
Deepgreen DB: Greenplum at Speed
- Speaker:
- CK Tan
- System:
- Vitesse
- Video:
- YouTube
Greenplum is an open source Postgres-based MPP solution that can scale to hundreds of nodes and petabytes of data. Deepgreen DB is an optimized version of Greenplum. On top of a mature, market-tested data warehouse, Deepgreen DB adds data-centric code generation for speed, columnar external data engine, new interconnect and SQL-level integration with Go/Python. This talk will mainly recount the... Read More
Jun 8
2020
Finding Logic Bugs in Database Management Systems
- Speaker:
- Manuel Rigger
- System:
- SQLancer
- Video:
- YouTube
Database Management Systems (DBMS) are used ubiquitously for storing and retrieving data. It is critical that they function correctly --- incorrectly computed result sets (e.g., by omitting a row) can cause serious loss or damage. We refer to such defects as logic bugs. Despite their importance, finding logic bugs in production DBMS is a longstanding challenge. Existing techniques such as... Read More