Is there a limit to the number of tables I can have in BigQuery? I'm trying to create multiple small tables to reduce query costs. Thanks!
There is no limit to the number of tables. You might have problems querying them all since there is a 10k limit to the length of a query string.
Now it's:
Maximum number of tables referenced per query — 1,000
Maximum unresolved legacy SQL query length — 256 KB
Maximum unresolved standard SQL query length — 1 MB
Maximum resolved legacy and standard SQL query length — 12 MB
https://cloud.google.com/bigquery/quotas
There is no limit in number of tables you can create. If you have more than a few thousand tables, listing a dataset may be slow (and opening the UI might be slow), but otherwise you can create as many tables as you need.
You can see this document.
There is no maximum number of standard tables.
but partitioned table have limit. (4,000)
and if there are so many tables, api can be slow and have limitation to see table on console.
When you use an API call, enumeration performance slows as you approach 50,000 tables in a dataset. The Cloud Console can display up to 50,000 tables for each dataset.
Related
I am currently using big query to store the user information to compute aggregate results against huge log data . But since modifying the data is not possible. In order to overcome this I am planning to store each user record in separate table. I understand bigquery supports querying from multiple tables using which i can get all information. My doubt over here are
as the number of users grows will the performance deteriorate as compared to storing all the users in a singe table.if there any limitations on number of tables per dataset in biq query
Thanks in advance
From what I know - there is no hard limit on number of tables in dataset.
At the same time - Native BQ UI has limit of first 10,000 tables in dataset to show.
Another limits to consider (just few to mention):
* Daily update limit: 1,000 updates per table per day;
* Query (including referenced views) can reference up to 1,000 tables and not more;
* Each additional table involved in a query (with hundreds and hundreds tables) makes considerable impact on performance.
* even if each table is small enough - it still will be charged at min price of 10MB (even if it is just few KB)
Not knowing your exact scenario doesnt allow making some recommendation, but at least you've got answer on those items in your question.
Overall, idea of having table per user doesn't sound good to me
I'm using BigQuery for ~5 billion rows that can be partitioned on ~1 million keys.
Since our queries are usually by partition key, is it possible to create ~1 million tables (1 table / key) to limit the total number of bytes processed?
We also need to query all of the data together at times, which is easy to do by putting it all in one table, but I'm hoping to use the same platform for partitioned analysis as bulk analytics.
That might work, but partitioning your table this finely is highly discouraged. You might be better off partitioning your data into a smaller number of tables, say 10 or 100, and querying just the one(s) you need.
What do I mean by discouraged? First, each of those million tables will get charged a minimum of 10 MB for storage. So you'll get charged for 9 TB of storage, when you likely have a lot less data than that. Second, you'll likely hit rate limits when you try to create that many tables. Third, managing a million tables is very tricky; the BigQuery UI will likely not be much help. Fourth, you'll make engineers on the BigQuery exceedingly grumpy, and they'll start trying to figure out whether we need to raise the minimum size for tables.
Also, if you do want to sometimes query all of your data, partitioning this finely is likely going to make things difficult for you, unless you are willing to store your data multiple times. You can only reference 1000 tables in a query, and each one you reference causes you to take a performance hit.
I've been doing some load testing of AWS Redshift for a new application, and I noticed that it has a column limit of 1600 per table. Worse, queries slow down as the number of columns increases in a table.
What doesn't make any sense here is that Redshift is supposed to be a column-store database, and there shouldn't in theory be an I/O hit from columns that are not selected in a particular where clause.
More specifically, when TableName is 1600 columns, I found that the below query is substantially slower than if TableName were, say, 1000 columns and the same number of rows. As the number of columns decreases, performance improves.
SELECT COUNT(1) FROM TableName
WHERE ColumnName LIKE '%foo%'
My three questions are:
What's the deal? Why does Redshift have this limitation if it claims to be a column store?
Any suggestions for working around this limitation? Joins of multiple smaller tables seems to eventually approximate the performance of a single table. I haven't tried pivoting the data.
Does anyone have a suggestion for a fast, real-time performance, horizontally scalable column-store database that doesn't have the above limitations? All we're doing is count queries with simple where restrictions against approximately 10M (rows) x 2500 (columns) data.
I can't explain precisely why it slows down so much but I can verify that we've experienced the same thing.
I think part of the issue is that Redshift stores a minimum of 1MB per column per node. Having a lot of columns creates a lot of disk seek activity and I/O overhead.
1MB blocks are problematic because most of that will be empty space but it will still be read off of the disk
Having lots of blocks means that column data will not be located as close together so Redshift has to do a lot more work to find them.
Also, (just occurred to me) I suspect that Redshift's MVCC controls add a lot of overhead. It tries to ensure you get a consistent read while your query is executing and presumably that requires making a note of all the blocks for tables in your query, even blocks for columns that are not used. Why is an implicit table lock being released prior to end of transaction in RedShift?
FWIW, our columns were virtually all BOOLEAN and we've had very good results from compacting them (bit masking) into INT/BIGINTs and accessing the values using the bit-wise functions. One example table went from 1400 cols (~200GB) to ~60 cols (~25GB) and the query times improved more than 10x (30-40 down to 1-2 secs).
I've got some highly partitionable data that I'd like to store in BigQuery, where each partition would get its own table. My question is if BQ will support the number of tables I'll need.
With my data set, I'd be creating approximately 2,000 new tables daily. All tables would have a 390 day (13 month) expiration, so eventually there'd be a constant count of ~ 2,000 tables * 390 days = ~780,000 tables in this particular project.
I'd test this myself, but BQ only supports a max of 10,000 load jobs per project per day.
Does anyone have experience with this sort of table count? Is there any official table limit provided by Google?
There are projects with that number of distinct tables today. There is not currently a hard cap on the number of distinct tables.
Some related considerations that come to mind when you're contemplating representations that use that many tables:
A query (including referenced views) can currently only reference 1000 tables.
Datasets with large numbers of tables may exhibit problematic behavior when using table wildcard functions.
You may be oversharding. Rather than lots of individual tables, you may simply want to use a wider schema and fewer tables.
If you're heavily dependent on time intervals as a sharding consideration, you may also want to look at table decorators as a way of limiting the scope of data scans.
You may also want to collapse data over time into fewer, larger tables as they age and are less frequently accessed. For example, copy jobs can append multiple source tables into a single destination table.
Most limits can be raised in BigQuery, as long as you are using BigQuery right - the limits are there to prevent abuse and misuse.
A critical question here - how much data will each table handle? Having 780,000 tables with 10 rows isn't a good idea.
How many tables do you want to handle per query? There's a hard limit of 1,000 tables per query.
If you have an interesting use case that requires higher limits, getting a support contract and their advice is the best way of having default limits raised.
https://cloud.google.com/support/
Need to exceed the 8k record limit in SQL Server 2008 when using wide columns/sparse tables.
Long story new client old system using survey system, pivoting data so all answers are a column
I have 1500 columns and now I am getting
Cannot create a row that has sparse data of size 9652 which is greater than the allowable maximum sparse data size of 8019.
I need to exceed the 8k record limit if possible
Not possible, because SQL Server stores rows on 8K pages. The only way to do so would be to store some of the data off-row (e.g. using MAX or other LOB types for some of your columns). To your application, this will still look like it's on the same row, even though logically it is on a complete different area of disk.
If your sparse column set alone exceeds the limit, sorry, you'll need to look at a different way to store the data (either not pivoted, EAV, or simply use two tables joined by a key, each containing half of the column set). For the latter you can make this relatively transparent to users by using views and/or enforcing all data access / DML through stored procedures that understand the division.