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
Related
I tried to use clickhouse to store 4 billion data, deployed on a single machine, 48-core cpu and 256g memory, mechanical hard disk.
My data has ten columns, and I want to quickly search any column through SQL statements, such as:
select * from table where key='mykeyword'; or select * from table where school='Yale';
I use order by to establish a sort key, order by (key, school, ...)
But when I search, only the first field ordered by key has very high performance. When searching for other fields, the query speed is very slow or even memory overflow (the memory allocation is already large enough)
So ask every expert, does clickhouse support such high-performance search for each column index similar to mysql? I also tried to create a secondary index for each column through index, but the performance did not improve.
You should try to understand how works sparse primary indexes
and how exactly right ORDER BY clause in CREATE TABLE help your query performance.
Clickhouse never will works the same way as mysql
Try to use PRIMARY KEY and ORDER BY in CREATE TABLE statement
and use fields with low value cardinality on first order in PRIMARY KEY
don't try to use ALL
SELECT * ...
it's really antipattern
moreover, maybe secondary data skip index may help you (but i'm not sure)
https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree/#table_engine-mergetree-data_skipping-indexes
While improving the performance of a Kusto query, I came across the shuffle strategy for join/summarize. I can clearly see the performance benefits of this strategy for my query, which has high cardinality for the join/summarize key.
While reading the shuffle query Kusto documentation, it seemed that the strategy will be ignored when there are nested shuffle operators.
When the shuffled operator has other shuffle-able operators, like summarize or join, the query becomes more complex and then hint.strategy=shuffle won't be applied.
My query uses nested summarize and join (with shuffle) but I also clearly see performance gains. My query pattern:
Table1
| summarize hint.strategy=shuffle arg_max(Timestamp) by Device, Interface
| join hint.strategy=shuffle (Table2) on Device, Interface
Does a query like this benefit from shuffling?
Also, does the Kusto query planner avoid any problematic shuffle if present always? Basically I wanted to rest assured that there might only be perf issues with a wrongly used/authored shuffle and not data issues.
Please note that the article of shuffle query suggests to use hint.shufflekey in case you have nested summarize/join operators but it requires that the nested summarize/join operators have the same group-by/join key.
so in your example above, apply the following (I'm assumging that Device has a high cardinality (and you can remove/keep the shuffle strategy from the summarize, keeping/removing it will be the same behavior as long as you specify the shuffle key on the join which wraps this summarize):
Table1
| summarize arg_max(Timestamp) by Device, Interface
| join hint.shufflekey=Device (Table2) on Device, Interface
I'm doing a project in asp.net core 2.1 (EF, MVC, SQL Server) and have a table called Orders, which in the end will be a grid (i.e. ledger) of trades and different calculations on those numbers (no paging...so could run hundreds or thousands of records long).
In that Orders table, is a property/column named Size. Size will basically be a lot value from 0.01 to maybe 10.0 in increments of 0.01..so 1000 different values to start and I'm guessing 95% of people will use values less than 5.0.
So originally, I thought i would use an OrderSize join table like so with a FK constraint to the Order table on Size (i.e. SizeId):
SizeId (Int) Value (decimal(9,2))
1 0.01
2 0.02
...etc, etc, etc...
1000 10.0
That OrderSize table will most likely never change (i.e. ~1000 decimal records) and the Size value in the Orders table could get quite repetitive if just dumping decimals in there, hence the reason for the join table.
However, the more I'm learning about SQL, the more I realize I have no clue what I'm doing and the bytes of space I'm saving might create a whole other performance robbing situation or who knows what.
I'm guessing the SizeId Int for the join uses 4 bytes? then another 5 bytes for the actual decimal Value? I'm not even sure I'm saving much space?
I realize both methods will probably work ok, especially on smaller queries? However, what is technically the correct way to do this? And are there any other gotchas or no-nos I should be considering when eventually calculating my grid values, like you would in an account ledger (i.e. assuming the join is the way to go)? Thank you!
It really depends what is your main objective behind using a lookup table. If its only around your concerns around storage space, then there are other ways you can design your database (using partitions and archiving bigger tables on cheaper storage).
That would be more scalable than using the lookup table approach (what happens if there are more than one decimal field in the Orders table - do you create a lookup table for each decimal field?).
You will also have to consider indexes on the Orders table while joining to the OrderSize table if you decide to go through that route. It can potentially lead to more frequent index scans if the join key is not part of the index on Orders table thereby causing slower query performance.
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 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