Machine learning power combined with decades of human DBA expertise help to achieve the best results in your database optimization
Postgres.AI - automated Postgres administration
You need Postgres.ai automated Postgres administration tools if:
- you are concerned about how well your database is configured, and if it allows your business to increase in size and scale;
- you are interested in preventing performance bottlenecks before your business begins suffering from occurring problems
What We Do
Detect performance bottlenecks
3 artificial DBAs (parts of the platform) control all database performance aspects, find bottlenecks before they affect your system and propose fixes using machine learning models. They predict the effects of each proposed optimization
Smart query normalization, deep performance analysis, SQL-tuning assistant based on Machine Learning
Index set optimization
Index adviser for all types of Postgres indexes (multi-column, partial, btree, GIN, GiST, SP-GiST, brin)
Postgres parameters tuning
No more need to read tons of documentation to tune Postgres. Just define parameters of your system and get detailed recommendations
Detailed monitoring (Postgres-specific metrics)
WAL, checkpoints, autovacuum, replication, table and index bloat, SQL performance and more
ML-backed Database Experiments
Our artificial DBA can run experiments to verify proposed optimization and avoid mistakes (continuous database administration)
Highly automated postgres-checkup procedures help to quickly check wide variety of performance, scalability and HA/reliability aspects in heavy-loaded projects and reduce the risk of human errors.
Guidance and Training
Human experts with decades of DBA experience will guide you through the procedure and provide training to your engineers, if necessary.
Budget and Resource Optimization
The use of postgres.ai's framework for conducting database experiments helps to find optimal configuration, optimize SQL queries, and do capacity planning to scale your project better at reduced costs and resources.
"Nancy" – an Artificial DBA, Expert in Conducting Database Experiments
Database experiments are needed when you:
Add or remove indexes
For a new DB schema change, want to validate it and estimate migration time
Want to verify some query optimization ideas
Tune database configuration parameters
Do capacity planning and stress-test your DB in some environment
Plan to upgrade your DBMS to a new major version
Want to train ML model related to DB optimization
More about "Nancy"
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