Updating two tables
Updating two tables - Sex video chat live mobil
While our service can now do a single fast key lookup to get the materialized view from the cache instead of querying the DB, it still has to query the DB and populate the cache on a cache miss, so the complex DB queries remain.
To improve performance, these services often pre-compute materialized views and store them in caches.This materialized view cache belongs only to our new service, decoupling it a bit from other services.However, introducing this cache into our service presents some new problems.Because tables and logs are dual, this essentially replicates each table into a Kafka topic.Consumers of these topics can then replicate the original tables or transform them however they wish.Second, it’s expensive: these queries are aggregating a (potentially) large number of rows.
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front-end desktop and mobile services can quickly get user information to display, the HTTP API can get user information to return as JSON, etc. First off, it’s somewhat complex to assemble all of the data needed to fulfill the requests: we are performing multiple queries across multiple tables.
The User Information Service will provide a single HTTP resource: Let’s assume that the data this service needs is stored in a relational database (e.g. (Twitter’s actual data storage is probably not like this, but many existing systems that we’re all familiar with do follow this standard model, so let’s roll with it.) With this architecture we would likely end up with the following tables: A classic implementation of this service would likely end up querying the DB directly using SQL. This is a fairly simple example; if our service had different requirements, these aggregations could be much more complex, with grouping and filtering clauses, as well as joins across multiple tables.
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In this article, we’ll explore a few problems with the typical approach to populating these caches, and see the wide variety of new solutions to these problems that are made possible simply by sending data changes to Kafka topics.