Is there any performance difference between jpql and querydsl?
I use querydsl to create dynamic queries.
I usually use jpql.
I wonder if there is any valid performance between these two.
Related
Is using query.aggregate(pipeline) in mongoDB more efficient than using normal queries such as query.equalTo, or query.greaterThan?
Aggregate queries definitely require much less code, but that alone doesn't seem to justify the complexity they bring with all the additional parantheses and abbreviations.
Normal queries seem more straightforward, but are they inferior in performance? What is a good use case for aggregate queries vs normal ones?
I am a student who is learning spring and jpa recently. While developing 'get api' with conditions, I came to think about which method is advantageous in terms of performance.
When it is necessary to query data based on conditions, jpql or querydsl are usually used to generate dynamic queries. Can you tell me why generating a dynamic query like this and looking up only the necessary data is better than using the java stream filter() function after looking up the entire data?
Also, can you tell me why generating fewer queries is advantageous in terms of performance?
I know that generating fewer queries has a performance advantage, but I lack an understanding of why I say it has a performance advantage.
Can you tell me why generating a dynamic query like this and looking up only the necessary data is better than using the java stream filter() function after looking up the entire data?
In general addressing the database or any other external storage is much more expensive than most of operations on Java side because of networking latency. If you query all the data and use e.g. list.stream().filter() than the significant amount of data is transferred over the network. And if one vice versa queries only some data filtered on the DB side the transferred amount in lower.
Pay attention, that while this is true in general there might be a cases when filtering on Java side could be more effective. This is highly dependent on several things:
query complexity
amount of data
database structure (schema, indices, column types etc.)
As of number of queries here we have the same considerations: query execution costs, data transfer costs, so the less queries you have - the better. And again, this is not an axiom: in some cases having multiple lightweight queries with grouping/filtering on Java side might be faster, than one huge and complicated SQL-query.
Hi I am currently using nested terms aggregations (triple or more) to query elasticsearch. I would rather use the composite aggregation with 3+ source fields that i just discovered since it is way more manageable in my opinion, but I was wondering if performance-wise this is a bad choice. Any recommendation ?
Do more like this queries in Elasticsearch make use of term vectors if these ar activated?
Yes.
The underlying Lucene MLT implementation also provides setMaxNumTokensParsed to provide some control over performance when term vectors are not available.
I know that when joining across multiple tables, performance is dependent upon the order in which they are joined. What factors should I consider when joining tables?
Most modern RDBM's optimize the query based upon which tables are joined, the indexes used, table statistics, etc. They rarely, if ever, differ in their final execution plan based upon the order of the joins in the query.
SQL is designed to be declarative; you specify what you want, not (in most cases) how to get it. While there are things like index hints that can allow you to direct the optimizer to use or avoid specific indexes, by and large you can leave that work to the engine and be about the business of writing your queries.
In the end, running different versions of your queries within SQL Server Management Studio and viewing the actual execution plans is the only way to tell if order can truly make a difference.
As far as I know, the join order has no effect on query performance. The query engine will parse the query and execute it in the way it believes is the most efficient. If you want, try writing the query using different join orders and look at the execution plan. They should be the same.
See this article: http://sql-4-life.blogspot.com/2009/03/order-of-inner-joins.html