The project was aimed at migrating data from an Oracle database management system (DBMS) to a PostgreSQL one. However, the migration will result in data distortion, which would lead to negative results – in particular, the growth of processing queues and the failure to achieve the required functionality.
This issue can be addressed by performing a VACUUM ANALYZE on the database objects to build up-to-date statistics for generating query plans. The process can also be performed automatically (autovacuum), but this takes more time. During this period, performance degradation of the PostgreSQL DBMS may occur.
Additionally, it was found that an index required for heavy requests was absent. After the index was created, the load on the DBMS significantly decreased.
duration6 months
technologies
Linux
Oracle
PostgreSQL
Python
Golang
ELK
Other:
Ansible
Results
The testing, migration, and disaster recovery plans of the system have been adjusted by our team;
The statistics on the objects of the entire database and statistics collection settings for large tables have been adjusted;
DBMS monitoring metrics have been improved – in particular, information about long-term locks, requests, and the connection pool has been collected and alert rules for such situations have been developed;
Long-term blocking in the database during integration with the Transaction information system has been optimized;
The data for locking has been transferred from the main table to a new one so that the row blocking processes do not affect other functional blocks of the information system;
Recommendations on changing the configuration of the PostgreSQL DBMS have been proposed based on the results of the study.
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Weitere Schritte
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