Evolution of logging: from relational database to search engines. A microservices In the first versions of InterBank RS a relational database was usdto store the system log. Despite the simplicity of implementation there were only a few interconnectdtables such a solution had a number of limitations: in some DBMS the functionality for full-text search is very limitdso it was not always possible to efficiently implement search using various filtering methods for example specifying search patterns in the form of regular expressions; to organize an efficient search in various fields of the system log tables it was necessary.
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To create a large number of indexes which taking into account the high load on writing to these tables the number of records adddcan reach hundreds and thousands per second ldto a significant increase in Cape Verde Email List the load on the DBMS associatdwith the nedto maintain indexes are up to date; full-text search modules are not always includdin the standard DBMS delivery so you had to buy additional licenses for example Oracle Text; archiving and backing up the system log requirdthe involvement of database administrators. Basically these limitations are relatdto the universality of the usdDBMS such as Oracle and Postgres. They do an excellent job of storing most business data. Only syslog events have one significant difference: they are immutable over time like logs.
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Important Web Design Questions to AskThus the performance of the data update command SQL Update command providdby the DBMS does not matter to the system log. Another characteristic feature BTC Email List is that events are generatdby the system constantly and in large numbers. As a result the system log takes up significantly more disk space than all other business data. The solution to this problem is predictable and lies in using a separate DBMS for the system log or even a system focusdon working with large amounts of static data and most importantly with fast search. At the moment several search engines have become widespread: Sphinx Solr Elasticsearch.
