Strong consistency is when a change has an immediate effect. This is most often noticeable with read-after-write patterns where what was just written (e.g., to a database) is immediately available to be read back. Eventual consistency is the opposite, when a change does not have an immediate effect. There is some delay between a change being made and it being possible to observe. To return all of the issues that were updated from the previous day. You can combine with whichever queries you want to return the appropriate issues for the previous day.

The "IN" operator is used to search for issues where the value of the specified field is one of multiple specified values. The values are specified as a comma-delimited list, surrounded by parentheses. This query can then be executed before providing the result to the user. Stage 1 what are JQL queries and how to use them consists of the majority of the total JQL results, making this Stage 2 query fast to execute, as it does not filter many issues from the results set. Jira Cloud poses some unique database scaling challenges, due to high query complexity causing high database load and slow queries.

Commonly used functions when writing SLA queries

Once a filter is created, you can select it to share it with other users. If you find yourself needing to search the same query on a regular basis, you can save it for later use. Although Jira JQL is pretty good at narrowing down from huge piles of issues, sometimes your query will still return a volume of issues that’s simply too large. A function is a pre-made command that users can plug into their query to return certain values.

It is also frequently executed in places you would not expect, such as when loading an issue. Simple queries (basic search) are a combination of fields and operators. A simple query in JQL is known as a “clause” and it consists of a field, followed by an operator, followed by one or more values or functions. JQL queries have an order of elements that need to be followed when creating SLAs (service level agreements).

Epic link

Not only do we want to avoid regressions like this, but we also want to have higher throughput – that is, more users able to execute more queries with the same or less load. Caching too many issues is a problem of both data size in the cache and the size of the query. JDBC limits the number of values in an in clause being sent to Postgres to values (Short.MAX_VALUE).