Cost optimization is critical when using BigQuery, as it can be expensive if used inefficiently. When implemented correctly, these best practices reduce BigQuery costs and optimize queries.
- Storage Management: One way to reduce BigQuery costs is to manage your storage efficiently. You can define an expiration time for tables or partitions if you just need current or recently updated data, regardless of the historical data. BigQuery is built from the ground up to save money on data storage that is no longer in use. By default, every table or partition in BQ that hasn’t been touched in at least 90 days will be moved to long-term storage, which costs $0.01 per GB per month, or 50% less than usual prices. You may also export BigQuery data to Google Cloud Storage for a lower cost if you have older data that you need to maintain in case you need to consult it or just for general governance concerns.
- Automatic View Creation: Generating views in BigQuery is quite similar to creating tables. As a result, instead of manually allowing views access to dependent datasets in BigQuery UI, if a data pipeline automates view generation, it is recommended to automate granting views access to dependent datasets as part of the same pipeline.
- Partitioning Tables: You can partition BigQuery data by a date column or by load time. Since it only retrieves the partitions required by the query, this increases performance and lowers the cost of a query.
- Clustering Tables: Clustered columns can be used to organize data in BigQuery. For example, you can organize employee data by department, and if a query is run on that department, BigQuery just reads until it reaches the filtered department, skipping the rest of the data, avoiding wasteful data scans.
- Nesting and Repeating Data: This is among the most significant ETL guiding principles for Google BigQuery. When the data is denormalized, Google BigQuery performs the best. Denormalize the data instead of retaining relationships and take leverage of nested and repeated fields.
- Slots: The computing power required to perform a SQL query in BigQuery is referred to as slots. In terms of pricing and resource allocation, slots are extremely important. BigQuery is in charge of slot allocation, and the amount of slots allotted to a job is determined by two factors: Query Size and Data Complexity. In addition to consumption-based pricing, BigQuery also offers flat-rate pricing, which allows you to purchase a set number of slots over a set period of time, giving you additional flexibility in capital planning.
- Streaming Inserts: A load task will be generated to read data directly and insert it into the table in batch mode when inserting data into a Google BigQuery table. Using streaming data, we will be able to query data without having to wait for the load process to finish.
Listed here are a few key things to keep in mind when using BigQuery:
- Avoid using SELECT * or unnecessary columns in your queries, as this can increase the amount of data read and processed, leading to higher costs.
- Use table partitioning and clustering to reduce the amount of data scanned by your queries.
- Use the correct data types to reduce storage costs.
- Use streaming inserts judiciously and consider batch loading data instead.
- Delete unused tables and partitions regularly to avoid incurring unnecessary storage costs.
- Use workload management (WLM) to manage query resources and avoid over-provisioning.
- Use query caching to avoid recomputing data that has already been processed.
- Use the BigQuery pricing calculator to estimate costs before running large queries or loading large datasets.
- Monitor query usage and identify long-running or expensive queries for optimization.
- Consider using cost-saving strategies such as data retention policies and data lifecycle management to manage data storage costs.
Remember that cost optimization is an ongoing process, and you should continually monitor and adjust your usage to ensure that you are getting the most value for your investment in BigQuery.