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.
Related
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.
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 get this error when trying to run a lot of import CSV jobs on BigQuery date-partitioned with a custom Timestamp column.
Your table exceeded quota for Number of partition modifications to a column partitioned table
Full error below:
{Location: "partition_modifications_per_column_partitioned_table.long"; Message: "Quota exceeded: Your table exceeded quota for Number of partition modifications to a column partitioned table. For more information, see https://cloud.google.com/bigquery/troubleshooting-errors"; Reason: "quotaExceeded"}
It is not clear to me on: What is the quota for Number of partition modifications? and how is it being exceeded?
Thanks!
What is the quota for Number of partition modifications?
See Quotas for Partitioned tables
In particular:
Maximum number of partitions modified by a single job — 2,000
Each job operation (query or load) can affect a maximum of 2,000 partitions. Any query or load job that affects more than 2,000 partitions is rejected by Google BigQuery.
Maximum number of partition modifications per day per table — 5,000
You are limited to a total of 5,000 partition modifications per day for a partitioned table. A partition can be modified by using an operation that appends to or overwrites data in the partition. Operations that modify partitions include: a load job, a query that writes results to a partition, or a DML statement (INSERT, DELETE, UPDATE, or MERGE) that modifies data in a partition.
You can see more details in above link
If you're gonna change the data often I strongly suggest you delete the table and simply upload it again with the new values. Every time you upload a new table, you get the limit refreshed.
Google BigQuery has no primary key or unique constraints.
We cannot use traditional SQL options such as insert ignore or insert on duplicate key update so how do you prevent duplicate records being inserted into Google BigQuery?
If I have to call delete first (based on unique key in my own system) and then insert to prevent duplicate records being inserted into bigquery, wouldn't that that be too inefficient? I would assume that insert is the cheapest operation, no query, just append data. For each insert if I have to call delete, it will be too inefficient and cost us extra money.
What is your advice and suggestions based on your experience?
It would be nice that bigquery has primary key, but it might be conflict with the algorithms/data structure that bigquery is based on?
So let's clear some facts up in the first place.
Bigquery is a managed data warehouse suitable for large datasets, and it's complementary to a traditional database, rather than a replacement.
Up until early 2020 there was only a maximum of 96 DML (update,delete) operations on a table per day. That low limited forced you to think of BQ as a data lake. That limit has been removed but it demonstrates that the early design of the system was oriented around "append-only".
So, on BigQuery, you actually let all data in, and favor an append-only design. That means that by design you have a database that holds a new row for every update. Hence if you want to use the latest data, you need to pick the last row and use that.
We actually leverage insights from every new update we add to the same row. For example, we can detect how long it took for the end-user to choose his/her country at signup flow. Because we have a dropdown of countries, it took some time until he/she scrolled to the right country, and metrics show this, because we ended up in BQ with two rows, one prior country selected, and one after country selected and based on time selection we were able to optimize the process. Now on our country drop-down we have first 5 most recent/frequent countries listed, so those users no longer need to scroll and pick a country; it's faster.
"Bulk Delete and Insert" is the approach I am using to avoid the duplicated records. And Google's own "Youtube BigQuery Transfer Services" is using "Bulk Delete and Insert" too.
"Youtube BigQuery Transfer Services" push daily reports to the same set of report tables every day. Each record has a column "date".
When we run Youtube Bigquery Transfer backfill (ask youtube bigquery transfer to push the reports for certain dates again.) Youtube BigQury Transfer services will first, delete the full dataset for that date in the report tables and then insert the full dataset of that date back to the report tables again.
Another approach is drop the results table (if it already exists) first, and then re-create the results table and re-input the results into the tables again. I used this approach a lot. Everyday, I have my process data results saved in some results tables in the daily dataset. If I rerun the process for that day, my script will check if the results tables for that day exist or not. If table exists for that day, delete it and then re-create a fresh new table, and then reinput the process results to the new created table.
BigQuery now doesn't have DML limits.
https://cloud.google.com/blog/products/data-analytics/dml-without-limits-now-in-bigquery
I'm looking for the price of data importation from Cloud Storage to Big Query (through "bq import").
There is no "update" statement in BigQuery, so I want to drop my table and recreate-it from scratch.
Thanks,
Romain.
As stated in the documentation, importing data is free. Only storing or querying it is charged.
https://cloud.google.com/bigquery/docs/updating-data
There is update statement in BigQuery now.
But the quota is low. So yes, we would drop table and recreate table sometimes, instead of using update.
https://cloud.google.com/bigquery/quotas
Data Manipulation Language statements
The following limits apply to Data Manipulation Language (DML).
Maximum UPDATE/DELETE statements per day per table: 96
Maximum UPDATE/DELETE statements per day per project: 10,000
Maximum INSERT statements per day per table: 1,000