When I try to create a view which query more than 600 tables, BigQuery was running for a long time and response is :
BigQuery error in mk operation: Backend Error.
the query itself is like:
'select col1,col2,col3 from t1,t2,t3......t600'
I suspect the operation is timing out. The limit here is whether validating the view query can be completed within the deadline limits for a single synchronous request like view creation. This many tables may just be too many.
A potential work-around might be to shard this view: create smaller view tables, then a single view of the set of smaller views.
An alternate solution would be to explore your data layout. Perhaps you don't need 600 tables to hold your data? The BigQuery team announced at GCP Next 2016 that table partitioning by date will be coming soon, so if you are sharding your tables by day and need to reference years of data, then there will be a single-table solution for you soon.
Related
I have data about contacts in Salesforce. I also have data about the contacts in Intercom/Zendesk. I want to create a denormalized table where the data in Salesforce and Intercom is both merged into a single table so I can query about the contact. Imagine, I dumped the Salesforce data into a Bigquery table. The problem is that the we might not dump Intercom/Zendesk until later. So we may only add Salesforce data into a Bigquery table now. And later we may add Intercom data. My question is how to merge these (existing data in Salesforce BQ table and new data from Intercom)? Assume that the Email is the primary key in both 3rd party sources and we can join them.
Do we need to take the Salesforce data out of the BQ table and run it through some tool to merge both tables and create a new table in BQ?
What will happen if we keep getting new data in both Salesforce and Intercom?
Your case seems to be a good use case for Views.
A view is basically a virtual table that points to a query. You can define a view based on a query (lets call it query_1) and then you will be able to see that view as a table. However every time you run a query (lets call it query_2) using that view as source, internally BigQuery will execute query_1 and then execute your query_2 against the results of query_1.
In your case, you could create a query that use join to merge your tables and save this query as a view. You can create a view by clicking on Save view in the BigQuery console just like in the image below and then fill some required fields before saving.
In BigQuery there are also Materialized Views, that implements some cache technologies in order to make the view more similar to a table.
Some benefits of materialized views are:
Reduction in the execution time and cost for queries with aggregate functions. The largest benefit is gained when a query's computation
cost is high and the resulting data set is small.
Automatic and transparent BigQuery optimization because the optimizer uses a materialized view, if available, to improve the query
execution plan. This optimization does not require any changes to the
queries.
The same resilience and high availability as BigQuery tables.
To create a materialized view you have to run the below command:
CREATE MATERIALIZED VIEW project-id.my_dataset.my_mv_table
AS <my-query>
Finally, I would like to paste here the reference links for both views and materialized views in BigQuery. I suggest that you take a look at it and decide which one fits in your use case.
You can read more about querying Google Cloud Storage https://cloud.google.com/bigquery/external-data-cloud-storage.
You can take the extracts and place them into Google Cloud Storage under buckets i.e. Salesforce bucket and Zendesk bucket.
Once the files are available, you can create external tables on those bucket(1 table for each bucket) so that you would be able to query them independently.
Once you can query them , you can perform joins like normal tables.
You can replace the files in buckets when new data comes.
Our BigQuery schema is heavily nested/repeated and constantly changes. For example, a new page, form, or user-info field to the website would correspond to new columns for in BigQuery. Also if we stop using a certain form, the corresponding deprecated columns will be there forever because you can't delete columns in Bigquery.
So we're going to eventually result in tables with hundreds of columns, many of which are deprecated, which doesn't seem like a good solution.
The primary alternative I'm looking into is to store everything as json (for example where each Bigquery table will just have two columns, one for timestamp and another for the json data). Then batch jobs that we have running every 10minutes will perform joins/queries and write to aggregated tables. But with this method, I'm concerned about increasing query-job costs.
Some background info:
Our data comes in as protobuf and we update our bigquery schema based off the protobuf schema updates.
I know one obvious solution is to not use BigQuery and just use a document storage instead, but we use Bigquery as both a data lake and also as a data warehouse for BI and building Tableau reports off of. So we have jobs that aggregates raw data into tables that serve Tableau.
The top answer here doesn't work that well for us because the data we get can be heavily nested with repeats: BigQuery: Create column of JSON datatype
You are already well prepared, you layout several options in your question.
You could go with the JSON table and to maintain low costs
you can use a partition table
you can cluster your table
so instead of having just two timestamp+json column I would add 1 partitioned column and 5 cluster colums as well. Eventually even use yearly suffixed tables. This way you have at least 6 dimensions to scan only limited number of rows for rematerialization.
The other would be to change your model, and do an event processing middle-layer. You could first wire all your events either to Dataflow or Pub/Sub then process it there and write to bigquery as a new schema. This script would be able to create tables on the fly with the schema you code in your engine.
Btw you can remove columns, that's rematerialization, you can rewrite the same table with a query. You can rematerialize to remove duplicate rows as well.
I think this use case can be implemeted using Dataflow (or Apache Beam) with Dynamic Destination feature in it. The steps of dataflow would be like:
read the event/json from pubsub
flattened the events and put filter on the columns which you want to insert into BQ table.
With Dynamic Destination you will be able to insert the data into the respective tables
(if you have various event of various types). In Dynamic destination
you can specify the schema on the fly based on the fields in your
json
Get the failed insert records from the Dynamic
Destination and write it to a file of specific event type following some windowing based on your use case (How frequently you observe such issues).
read the file and update the schema once and load the file to that BQ table
I have implemented this logic in my use case and it is working perfectly fine.
I am trying to copy a BigQuery table using the API from one table to the other in the same dataset.
While copying big tables seems to work just fine, copying small tables with a limited number of rows (1-10) I noticed that the destination table comes out empty (created but 0 rows).
I get the same results using the API and the BigQuery management console.
The issue is replicated for any table in any dataset I have. Looks like a bug or a designed behavior.
Could not find any "minimum lines" directive in the docs.. am I missing something?
EDIT:
Screenshots
Original table: video_content_events with 2 rows
Copy table: copy111 with 0 rows
How are you populating the small tables? Are you perchance using streaming insert (bq insert from the command line tool, tabledata.insertAll method)? If so, per the documentation, data can take up to 90 minutes to be copyable/exportable:
https://cloud.google.com/bigquery/streaming-data-into-bigquery#dataavailability
I won't get super detailed, but the reason is that our copy and export operations are optimized to work on materialized files. Data within our streaming buffers are stored in a completely different system, and thus aren't picked up until the buffers are flushed into the traditional storage mechanism. That said, we are working on removing the copy/export delay.
If you aren't using streaming insert to populate the table, then definitely contact support/file a bug here.
There is no minimum records limit to copy the table within the same dataset or over a different dataset. This applies both for the API and the BigQuery UI. I just replicated your scenario of creating a new table with just 2 records and I was able to successfully copy the table to another table using UI.
Attaching screenshot
I tried to copy to a timestamp partitioned table. I messed up the timestamp, and 1000 x current timestamp. Guess it is beyond BigQuery's max partition range. Despite copy job success, no data is actually loaded to the destination table.
As part of our Bigquery solution we have a cron job which checks the latest table created in a dataset and will create more if this table is out of date.This check is done with the following query
SELECT table_id FROM [dataset.__TABLES_SUMMARY__] WHERE table_id LIKE 'table_root%' ORDER BY creation_time DESC LIMIT 1
Our integration tests have recently been throwing errors because this query is hitting Bigquery's internal cache even though running the query against the underlying table would provide a different result. This caching also occurs if I run this query in the web interface from Google cloud console.
If I specify for the query not to cache using the
queryRequest.setUseQueryCache(false)
flag in the code then the tests pass correctly.
My understanding was that Bigquery automatic caching would not occur if running the query against the underlying table would provide a different result. Am I incorrect in this assumption in which case when does it occur or is this a bug?
Well the answer for your question is: you are doing conceptually wrong. You always need to set the no cache param if you want no cache data. Even on the web UI there are options you need to use. The default is to use the cached version.
But, fundamentally you need to change the process and use the recent features:
Automatic table creation using template tables
A common usage pattern for streaming data into BigQuery is to split a logical table into many smaller tables, either for creating smaller sets of data (e.g., by date or by user ID) or for scalability (e.g., streaming more than the current limit of 100,000 rows per second). To split a table into many smaller tables without adding complex client-side code, use the BigQuery template tables feature to let BigQuery create the tables for you.
To use a template table via the BigQuery API, add a templateSuffix parameter to your insertAll request
By using a template table, you avoid the overhead of creating each table individually and specifying the schema for each table. You need only create a single template, and supply different suffixes so that BigQuery can create the new tables for you. BigQuery places the tables in the same project and dataset. Templates also make it easier to update the schema because you need only update the template table.
Tables created via template tables are usually available within a few seconds.
This way you don't need to have a cron, as it will automatically create the missing tables.
Read more here: https://cloud.google.com/bigquery/streaming-data-into-bigquery#template-tables
I have 14 tables in BQ, which are updated several times a day.
Via JOIN of three of them, I have created a new one.
My question is, would this new table be updated each time new data are pushed into BQ tables on which it is based on? If not, is there way, how to make this JOIN "live" so the newly created table will be updated automatically?
Thank you!
BigQuery also supports views, virtual tables defined by a SQL query.
BigQuery's views are logical views, not materialized views, which means that the query that defines the view is re-executed every time the view is queried. Queries are billed according to the total amount of data in all table fields referenced directly or indirectly by the top-level query.
BigQuery supports up to eight levels of nested views.
You can create a view or materialized view so that your required data setup gets updated instantly but this queries underlying tables so beware of joining massive tables.
For more complex table sync from/to BQ and other Apps (two-way sync), I finally used https://www.stacksync.cloud/
It offers real-time update and eventually two-way sync. Check it out too for the less technical folks!