Towards 1M writes/sec: Scaling PostgreSQL using Citus MX
Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.
One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.
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Jasonysli Tencent
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Dmitry Lebedev BestPlace
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But just PostGIS is not enough. You need tools to import, verify and visualize the data. It's critically important to visualize the data live, to debug your calculations and shorten iterations. I'll describe all these steps:
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- 3rd party APIs for calculations.
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- Debugging the calculations: live visualization (Arc, QGIS, NextGIS Web)
- Scripting and minimizing the chores: Makefile, Gulp
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Alexander Korotkov Postgres Professional
RUM indexes and their applications
I want to present a new custom access method, which extends the current GIN capabilities using additional information stored in posting tree/list. For example, positional information as an additional information allows new AM returns results in relevance order, which could considerably improve execution time of full text queries.
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Dmitry Vagin Avito
Monitoring PostgreSQL in Avito.ru, with case studies
A short talk about collecting data and monitoring database workload in Avito. Exporting metrics from stored procedures to Graphite. Collecting and visualizing pg_stat* metrics in Grafana. Case studies.
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