I would to know more about query optimizer in sqlite. For order of join, on the website there are only
When selecting the order of tables in a join, SQLite uses an efficient
polynomial-time algorithm. Because of this, SQLite is able to plan
queries with 50- or 60-way joins in a matter of microseconds.
but where are the details, what is the specific function?
See
The SQLite Query Planner: Joins:
http://www.sqlite.org/optoverview.html#joins
The Next Generation Query Planner:
http://www.sqlite.org/queryplanner-ng.html
Related
I have a dataflow with a few joins and when making the join #5, the number of row goes from 10,000 to 320,000 (to make an example of how the quantity is increased), but after that i have more joins to make so the dataflow is taking longer to complete.
What I do is to add an Aggregate transformation after the joins, to groupby the field that I will use later, using that in a way that I use a SELECT DISTINCT in a query on the database, but still taking soooo long to finish.
How can make this dataflow run faster?
Should I use an Aggregate (and groupby the fields) between every join, to avoid the duplicates or just add the Aggregate (and groupby the fields...) after the join where the rows starts to increase?
Thanks.
Can you switch to Lookups instead of Join and then choose "run single row". That provides the SELECT DISTINCT capability in a single step.
Also, to speed up the processing end-to-end, try bumping up to memory optimized and raise the core count.
I'm a little unfamiliar with ClickHouse and still study it by trial and error. Got a question about it.
Talking about the star scheme of data representations, with dimensions and facts. Currently, I keep everything in PostgreSQL, but OLAP queries with aggregations start to show bad timing, so I'm going to move some fact tables to ClickHouse. Initial tests of CH show incredible performance, however, in real life the queries should include joins to dimension tables from PostgreSQL. I know I can connect them as dictionaries.
Question: I found that using dictionaries I can make requests similar to LEFT JOINs in good old RDBMS, ie values from resultset could be joined with corresponding values from the dictionary. But can they be filtered by some restrictions on dictionary keys (as in INNER JOIN)? For example, in PostgreSQL I have a table users (id, name, ...) and in ClickHouse I have table visits (user_id, source, medium, session_time, timestamp, ...) with metrics about their visits to the site. Can I make a query to CH to fetch aggregated metrics (number of daily visits for given date range) of users which name matches some condition (LIKE "EVE%" for example)?
It sounds like ODBC table function is what you're looking for. ClickHouse have a bunch of table functions which work like Postgres foreign tables. The setup is similar to Dictionaries but you gain the traditional JOIN behavior. It currently doesn't show up in the official document. You can refer to this https://github.com/yandex/ClickHouse/blob/master/dbms/tests/integration/test_odbc_interaction/test.py#L84 . And in near future (this year), ClickHouse will have standard JOIN statement supported.
The dictionary will basically replace the value first. As I understand it your dictionary would be based off your users table.
Here is an example. Hopefully I am understanding your question.
select dictGetString('accountidmap', 'domain', tuple(toString(account_id))) AS domain, sum(session) as sessions from session_distributed where date = '2018-10-15' and like(domain, '%cats%') group by domain
This is a real query on our database so If there is something you want to try/confirm let me know
If I have a set of tables that I need to extract from an Oracle server, is it always more efficient to join the tables within Oracle and have the system return the joined table, or are there cases where it would be more efficient to return two tables into R (or python) and merge them within R/Python locally?
For this discussion, let's presume that the two servers are equivalent and both have similar access to the storage systems.
I will not go into the efficiencies of joining itself but anytime you are moving data from a database into R kep the size into account. If the dataset after joining will be much smaller (maybe after an inner join) it might be best to join in db. If the data is going to expand significantly after join (say cross join) then joining it after extraction might be better. If there is not much difference then my preference would be to join in db as it can be better optimized. In fact if the data is already in db try to do as much of data preprocessing before extracting it out.
I created COMPREHENSIVE design using a list of 80-85 Queries. Most of them are too big , like 300 - 400 lines each . And most of the Queries has a lot of inner Queries . My Query is that does DBD takes into account the inner Queries too for Projection creation, AS most of the inner Query 's Explain plan does seem to suggest it.
It does take into account queries with joins, in the sense that it tries to look for a common key. After DBD is finished with the design, it's always a good idea to review the design before deploying.
The best way to optimize for joins is by using primary & foreign keys, and possibly pre-join projections.
Another approach is to look at your actual schema design as the goal should be to have joins to perform locally. You may want to replicate smaller tables or have a single, very large table replicated. This will still allow joins to happen locally.
Some articles which may help in optimizing joins:
Join Operator Overview
Pre-join Projections Overview
I'm curious about what's the performance change if adding more joins? is there join number limitation? e.g. if greater than a value, the performance will be degraded. thanks.
Maximum Number Of Tables In A Join
SQLite does not support joins containing more than 64 tables. This limit arises from the fact that the SQLite code generator uses bitmaps with one bit per join-table in the query optimizer.
SQLite uses a very efficient O(N²) greedy algorithm for determining the order of tables in a join and so a large join can be prepared quickly. Hence, there is no mechanism to raise or lower the limit on the number of tables in a join.
see :http://www.sqlite.org/limits.html