High concurrency with Postgres
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This document discusses how Bank of Brazil uses PostgreSQL to process a high volume of documents every day as part of its multi-document processing application. It faces challenges from high concurrency with over 22,000 stations accessing the single PostgreSQL database. To address this, Bank of Brazil implemented solutions like PgBouncer for connection pooling, caching with Memcached, advisory locks, temporary tables, and partitioning of 24 tables. These solutions helped Bank of Brazil scale its PostgreSQL database to meet the heavy processing demands of scanning, recognizing, and managing documents from its over 6,000 branches.
High concurrency with Postgres
- 1. High Concurrency with Postgres Bank of Brazil in real life F´abio Telles Rodriguez Timbira - The Brasilian Postgres Company February 4, 2016 PGConf Russia
- 2. About me F´abio Telles Rodrigues Open software evangelist +15 years PostgreSQL DBA +10 year Contributor of the brazilian PostgreSQL community Blog: http://savepoint.blog.br (Portuguese only) @telles
- 3. Timbira http://www.timbira.com.br The Brasilian Postgres Company Consulting / Development / Support Partnership with software development companies In-Company and On-Line training
- 4. About these talk Outline About Bank of Brasil Multi Document Processing Challenges Adopted solutions
- 5. Bank of Brasil Founded at 1808, oldest bank in Brazil 59% of shares belong to the government The only bank that has branches in all Brazilian cities more then 110.000 direct employees
- 6. Multi Document Processing app Developed by Bull (now Atos) Development started as a client/server application 15 years ago Mostly written in C++ Perform a decentralized dematerialization of documents combined with a complex central processing of then Each document have differentdiferent workflows
- 7. Multi Document Processing app Documents are captured using scanners at branches Local image recognition (reach about 85% of success) Shipping images and meta data to a central point (images are not stored inside database) Central image recognition using third-party tools like ABBYY and A2iA. Manual image recognition Other manual interactions like authorizations and deviations Thousands of business rules for each document Complex interaction with many legacy systems
- 8. Real life 6,364 branches 22,432 stations using application (will double by the end of the year) 25 servers making central recognition 12 application servers 200 stations making manual recognition Hundreds of leaders requesting reports on peak processing One PostgreSQL database
- 9. The database PostgreSQL 9.3 228 tables 23 triggers 17 sequences 402 functions
- 10. In a regular day check clearing: 600,000 (2 milion in the busiest day) escrow checks: 70,000 signature cards: 30,000 non financial documents: 50,000 Critical window between 4pm to 7pm Growth of 10GB 80GB of archives generated
- 11. Challenges High number of connections Locks in processing queues High number of transactions Small processing window Many heavy queries for reports Need to keep the information for two years in the database New features being implemented constantly
- 12. Adopted solutions 3 PgBouncer instances Mencached + Listen / Notify to spread information across the stations Strict control of transactions The queue for image recognition was implemented in memory and integrated inside database with PL/Peru using sockets Use of advisory locks in other queues Memory adjustments for specifc users Vaccum and fillfactor adjustments for speciic tables Partition on 24 tables Use of temporary tables and unlogged tables Redesign critical process
- 13. Questions Spasibo Thank you Obrigado F´abio Telles Rodriguez (telles@timbira.com.br) http://www.timbira.com.br