As title stated.
I'm curious about there's a python AIP (QueryJobConfig) that I can set the destination table where I can save the query result, so for this kind of saving how much will it cost in GCP?
There are two types of way that I can load or insert external data to BigQuery. (Streaming and Batch uploading)
However, using streaming insert might be costly.
To make it clear, saving the query result may consider as an insertion, so if I use this method to update a table frequently, will it be costly as doing the streaming insert.
BigQuery saves query results to a table, which can be either permanent or temporary.
Temporary table has a lifetime of 24 hours and you are not charged for storing temporary tables.
When you write query results to a permanent table, you can create/append/overwrite the table. When you specify a destination table for large query results, you are charged for storing the data. Storage pricing is based on the amount of data stored in your tables. Please, refer to official documentation describing BigQuery pricing.
Streaming insert can be done either using DataFlow jobs or by calling the BigQuery streaming insert API directly. You need to pay for streaming insert process ($0.01per 200 MB) and storage of the new data.
If you wish to estimate storage and query cost, please refer to official documentation.
I hope you find the above pieces of information useful.
Related
I have usecase for designing storage for 30 TB of text files as part of deploying data pipeline on Google cloud. My input data is in CSV format, and I want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which would be a better option in below for this use case?
Using Cloud Storage for storage and link permanent tables in Big Query for query or Using Cloud Big table for storage and installing HBaseShell on compute engine to query Big table data.
Based on my analysis in below for this specific usecase, I see below where cloudstorage can be queried in through BigQuery. Also, Bigtable supports CSV imports and querying. BigQuery limits also mention a maximum size per load job of 15 TB across all input files for CSV, JSON, and Avro based on the documentation, which means i could load mutiple load jobs if loading more than 15 TB, i assume.
https://cloud.google.com/bigquery/external-data-cloud-storage#temporary-tables
https://cloud.google.com/community/tutorials/cbt-import-csv
https://cloud.google.com/bigquery/quotas
So, does that mean I can use BigQuery for the above usecase?
The short answer is yes.
I wrote about this in:
https://medium.com/google-cloud/bigquery-lazy-data-loading-ddl-dml-partitions-and-half-a-trillion-wikipedia-pageviews-cd3eacd657b6
And when loading cluster your tables, for massive improvements in costs for the most common queries:
https://medium.com/google-cloud/bigquery-optimized-cluster-your-tables-65e2f684594b
In summary:
BigQuery can read CSVs and other files straight from GCS.
You can define a view that parses those CSVs in any way you might prefer, all within SQL.
You can run a CREATE TABLE statement to materialize the CSVs into BigQuery native tables for better performance and costs.
Instead of CREATE TABLE you can do imports via API, those are free (instead of cost of query for CREATE TABLE.
15 TB can be handled easily by BigQuery.
I have a data of around 150 GB data and I want to store that in bigquery using DML statements.
Here is the pricing model for that.
https://cloud.google.com/bigquery/pricing#dml
According to them they will charge for deleting the table via DML.
If I create a table with retention period will I be charged for that? considering I will always insert data. I am not bothering about cost for inserting data.
Based on the DML Specifications, Google will charge for the deletion of rows if done using DML statement (or using DELETE command in their SQL). The reason being: BigQuery will have to scan rows to delete them (like DELETE FROM mydataset.mytable WHERE id=xxx;, etc.), so you will have to pay for the number of bytes scanned before deleting the resulting rows.
You can always delete your entire table from your dataset for free by either using BigQuery UI or bq command line utility.
Also, you will be charged for the storage costs in BigQuery (irrespective of usage). Meaning: you will pay for the number of bytes your data is occupying on Google disks.
BigQuery charges for deleting from a table, not deleting a table. Executing a DROP TABLE statement is free.
Creating tables is free, unless you create a table from the result of a table, in which case see query pricing to calculate your costs.
The cost of storage is based on the number of bytes stored and how long you keep the data. See storage pricing for more details.
I'm wondering what kind of insert saving the results of a large query (with multiple joins and unions) to a destination (day partitioned) table is.
Currently on a GC VM I execute these queries, save the results in a local temporary CSV and upload these CSVs to their respective tables.
This is fairly ineffective (not as fast as it can be and uses quite a lot of VM resources). However, it is cheap, since CSV loading jobs are free. If I were to save the query results into a destination table (appending to old data which already consists of 100M+ rows), would those incur insertion streaming costs? This is what I'd like to avoid since $0.02/MB can rack up quite a bit since we're adding a lot of data on a daily basis.
Thanks for your help.
Inside Bigquery running a query and saving to destination table results you
query price (anyway you pay it)
storage price (new data gets accumulated to the table - choose partitioned)
no streaming costs
If you have data outside of BQ and you end up adding the data to BQ
if you use load (that's free)
streaming insert (has a cost component)
storage of the new data, the table you added
I'm wondering what kind of insert saving the results of a large query (with multiple joins and unions) to a destination (day partitioned) table is.
... if I were to save the query results into a destination table (appending to old data which already consists of 100M+ rows), would those incur insertion streaming costs?
Setting the destination table for query job is the most effective way of getting result of that query being added to the existing table. It DOES NOT incur any extra cost related to streaming as there is no streaming happening here at all
I need to move BigQuery datasets with many tables (both partitioned and unpartitioned) from the US to the EU.
If the source table is unpartitioned, the documented way of bq extracting the data to GCS and bq loading it in another region works fine, so far so good.
If however the source table is partitioned, during the load step the mapping between data and partition is lost and I'll end up having all data within one partition.
Is there a good (automated) way of exporting and importing partitioned tables in BQ? Any pointers would be greatly appreciated!
There's a few ways to do this, but I would personally use Cloud Dataflow to solve it. You'll have to pay a little bit more for Dataflow, but you'll save a lot of time and scripting in the long run.
High level:
Spin up a Dataflow pipeline
Read partitioned table in US (possibly aliasing the _PARTITIONTIME to make it easier later)
Write results back to BigQuery using same partition.
It's basically the same as what was talked about here.
Another solution is to use DML to load the data, instead of load, https://cloud.google.com/bigquery/docs/using-dml-with-partitioned-tables. Since you have a timestamp column in the table to infer the partition, you can use
INSERT INTO PROJECT_ID.DATASET.mytable (_PARTITIONTIME, field1, field2) AS SELECT timestamp_column, 1, “one” FROM PROJECT_ID.DATASET.federated_table
You can define a permanent federated table, or a temporary one, https://cloud.google.com/bigquery/external-data-cloud-storage#permanent-tables. You'll need to pay for DML though, while load is free.
In traditional data modeling, I create hourly and daily rollup table to reduce data storage and improve query response time. However, the attempt to create similar rollup table easily run into "Response too large to return" error. What is the recommended method to create rollup table with BigQuery? I need to reduce data to reduce cost of storage and query.
Thx!
A recently announced BigQuery feature allows large results!
Now you can specify a flag and a destination table. Results of arbitrary size will be stored in the designated table.
https://developers.google.com/bigquery/docs/queries#largequeryresults
It sounds like you are appending all of your data to a single table, then want to create smaller tables to query over ... is that correct?
One option would be to load your data in the hourly slices, then create the daily and 'all' tables by performing table copy operations with write_disposition=WRITE_APPEND. Alternately, you can use multiple tables in your queries. For example select foo from table20130101,table20130102,table20130102. (Note this does not do a join, it does a UNION ALL. It is a quirk of the bigquery query syntax).
If it will be difficult to change the layout of your tables, there isn't currently support for larger query result sizes, but it is something that is one of our most requested features and we have it a high priority.
Also, creating smaller tables won't necessarily improve query performance, since bigquery processes queries in parallel to the extent possible. It won't reduce storage costs, unless you're only going to store part of the table. It will, of course, reduce the costs of a query, since running queries against larger tables is more expensive.
If you describe your scenario a bit more I may be able to offer more concrete advice.