Database driven development with postgresql & clojure
If you honestly evaluate most of our business applications, you will see that they first collect and import the data into a database and then send the same data in the opposite direction.
What if we don't build an ORM wall between the application and the database, but try using the symbiosis of their strong points and special features instead?
I will tell you how we use PostgreSQL and Clojure for building data-intensive medical applications. We will cover the following topics:
- functional relational programming
- jsonb for modeling complex data domains
- functional indexes and json-knife extension for jsonb search
- graphql implementation on PostgreSQL
- logical replication for building reactive integrations
- asynchronous JDBC-free connector to PostgreSQL on netty
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Другие доклады
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Ivan Frolkov Postgres Professional
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It is often required to asynchronously perform several transactions in a strictly defined sequence, not just a single transaction. There are several ways to achieve this, and one of the solutions available is the pgpro_scheduler module.
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Andrey Litunenko 2ГИС
How we said goodbye to MongoDB and switched to PostgreSQL
In this talk I will share the experience of migration, NoSQL data to a relational view convert, and tell you how we managed to speed up the application in 2 times.
Initially, we used PosgtgreSQL and MongoDB to store all the data. In practice, we found out that the separation is extremely inconvenient. We spent a lot of time and attention on it.
My talk will describe how we moved the data from MongoDB to PostgreSQL using mosql. As a result, all the data can be received by one query now, and the table schema ensures data consistency.
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Olivier Courtin DataPink
Advanced spatial analysis with PostgreSQL, PostGIS and Python
PostGIS is well known and widely used since two decades, as the best OpenSource database solution for Spatial Analysis. This talk will focus on: spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available); how to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)
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Dmitriy Sarafannikov Яндекс
How to save statistics during major update, and what can be the consequences
It's not a secret for anyone that statistics can not be transferred with a major upgrade. For small and not heavily loaded databases this is not a problem, you can quickly collect new statistics. But we have databases with a volume of about 5TB and a load of about 100k rps, for which it became a big problem: taking off without statistics, the replicas could not even replay WAL. In my report I'll tell you what tricks we went to upgrade these databases with requirements of 100% read only availability, about what mistakes were made, and about how these errors were painfully corrected. The result of these errors was the extension called "pg_dirty_hands", in which we will collect various hacks, which can be last resort to repair data corruption.