Seeking assistance with data partitioning on GCP

[NishinThattil]

To improve efficiency on GCP, we were looking at various options- especially in light of the recent pricing changes.

We noticed most tables/ data don't have partitions. Is it possible to add partitions in an automated way .

[ms4446]

Unfortunately, it is not possible to directly add partitions to an existing unpartitioned table in BigQuery. However, there are several approaches you can take to achieve similar results in an automated way:

1. Recreate the Table with Partitioning:

   • Create a new partitioned table with the desired partition key and schema, then copy data from the original table. This process can be automated using scripts with tools like the bq command-line tool or the BigQuery API.
   • Tools like Stitch can automate table recreation with partitioning and clustering.
   • Note: This involves replacing or deleting the old table after data transfer, so plan for data integrity and potential downtime.

2. Use a Partitioned View:

   • Create a view on top of the original table with a WHERE clause based on the desired partition key. This simulates a partitioned experience without physically altering the table.
   • While this avoids data copying, it might not offer the same performance or cost-saving benefits as true partitioning, since the underlying data remains unpartitioned.

3. Use External Data Sources:

   • If your data resides in Google Cloud Storage (GCS) with a directory structure reflecting your desired partition scheme, create an externally partitioned table on top of it.
   • This approach avoids data copying and allows BigQuery to leverage the partition information for improved performance.

4. Use Dataflow:

   • Leverage Google Cloud Dataflow for reading data from the unpartitioned table, applying partitioning logic, and writing it to a new partitioned table.
   • This method is more complex but offers flexibility for intricate scenarios.

Remember, optimizing partitioning can significantly improve query performance and cost efficiency in BigQuery. Choose the approach that best suits your needs or combine them depending on your specific use case. Always ensure to verify the latest documentation and best practices from Google Cloud's official resources, as they are regularly updated.

[NishinThattil]

We would like to partition table on the basis of columns related to ingestion/creation time and date on existing multiple tables in bigquery.

[ms4446]

Partitioning your BigQuery tables by ingestion/creation time and date is indeed a common and effective strategy to improve performance and cost efficiency. Given that you're dealing with multiple tables, here are some refined approaches to consider:

1. Individually Partition Each Table:

   • Recreate each table with the desired partition key (ingestion/creation time and date) and schema. This is a standard approach for optimizing query performance.
   • Automate this process using:
     • bq command-line tool: Write scripts to create new partitioned tables and transfer data from the original tables.
     • BigQuery API: Develop custom programs for table creation and data migration.
     • Dataflow: Construct pipelines for reading, partitioning, and writing data to new tables.
   • Important: Plan for potential downtime and ensure data integrity during the transfer process.

2. Use Partitioned Views:

   • Create views on top of each table with WHERE clauses based on the partition key. This simulates partitioning without altering the physical table structure.
   • Note: While this method avoids data movement, it might not offer the same level of performance benefits as actual partitioned tables.

3. Combine Partitioned Views and Materialized Views:

   • Implement partitioned views with WHERE clauses and layer materialized views on top for enhanced query performance.
   • This method balances performance gains with some level of data duplication and maintenance overhead.

4. External Partitioned Tables (If Data Resides in GCS):

   • If your data is already organized in GCS with a partition-like structure, create external partitioned tables to leverage this existing organization.
   • This approach is efficient for large datasets, improving performance without the need for data copying.

5. Consider Clustering:

   • In addition to partitioning, clustering tables by relevant columns (like ingestion date) can further optimize specific query types.
   • Clustering complements partitioning and can lead to significant performance improvements.

Choosing the Best Approach:

• Consider the number of tables, data volume, access patterns, desired performance improvement, acceptable downtime, and data movement.
• Analyze your query patterns to identify which queries will benefit most from partitioning.
• Estimate your data size and growth to choose a scalable method.
• Evaluate the potential performance impact, considering both query speed and cost reduction.

[NishinThattil]

can i get more clarity on BigQuery API: Develop custom programs for table creation and data migration.

[ms4446]

Developing custom programs using the BigQuery API for table creation and data migration offers a powerful and efficient way to automate the process. These programs interact with BigQuery's services to create partitioned tables and transfer data from existing sources, streamlining your workflow and eliminating manual tasks.

Utilizing BigQuery Client Libraries:

Google Cloud provides convenient client libraries for various programming languages like Python, Java, and Node.js. These libraries simplify interacting with the BigQuery API compared to directly using REST calls, providing idiomatic and familiar syntax for developers.

Setting Up the Environment:

1. Authentication: Obtain a service account key file from your Google Cloud project to authenticate your program with BigQuery.
2. Library Installation: Install the appropriate BigQuery client library for your chosen programming language.

Writing the Program:

1. Create a BigQuery Client: Initialize a BigQuery client object within your program to interact with the service.
2. Define Table Schema: Specify the schema for the new partitioned table, including the partitioning configuration (e.g., daily, monthly).
3. Create a New Table: Use the client to create the partitioned table in BigQuery based on the defined schema.
4. Data Transfer Logic: Implement code to read data from the existing source (table or other data store) and write it to the new partitioned table. This may involve querying data and performing insert operations.
5. Partition Handling: Leverage BigQuery's partitioning functionality by specifying the partitioning column and type during table creation. This efficiently organizes data for future access and analysis.

Ensuring Robustness and Performance:

1. Error Handling: Incorporate robust error handling to address potential issues like network errors or quota limitations.
2. Optimization: Optimize the data transfer process, particularly for large datasets. Consider batching data transfers or utilizing efficient query patterns to improve performance.

Testing and Deployment:

1. Testing: Thoroughly test the program in a development environment to confirm its functionality and accuracy. Ensure successful table creation, data migration, and absence of data loss or corruption.
2. Deployment: Once tested and validated, deploy the program to your production environment for automated table creation and data migration.

This approach offers significant advantages, including:

• Automation: Eliminates manual tasks and streamlines your workflow.
• Flexibility: Tailors table creation and data migration to your specific needs.
• Control: Provides complete control over the process and data flow.
• Scalability: Handles large datasets efficiently with optimized data transfer.

Additional Resources:

• BigQuery API Client Libraries: https://cloud.google.com/python/docs/reference/bigquery/latest
• BigQuery Migration Service: https://cloud.google.com/bigquery/docs/migration-intro
• BigQuery Storage API: https://cloud.google.com/bigquery/docs/reference/storage
• BigQuery Data Transfer Service: https://cloud.google.com/bigquery/docs/cloud-storage-transfer

[NishinThattil]

Is it possible to pass the table to be partitioned, along with the following details, as a CSV file?

project_id

dataset_id

extracted_base_table_name

[ms4446]

Yes, it is possible to pass the details of the tables to be partitioned as a CSV file and automate the process using a script. Here's a breakdown:

Prepare the CSV File:

1. Create a CSV file containing the following details for each table:

   • project_id
   • dataset_id
   • extracted_base_table_name

2. Example content:

project_id,dataset_id,extracted_base_table_name
project1,dataset1,table1
project2,dataset2,table2
...

Read the CSV File:

1. Write a script (e.g., Python, Java) using libraries like csv to read the file.

Initialize BigQuery Client:

1. Set up authentication with Google Cloud and initialize the BigQuery client library within your script.

Iterate Over CSV Rows:

1. For each row:
   • Extract project_id, dataset_id, and extracted_base_table_name.
   • Check if the table exists using the BigQuery client.

Create Partitioned Tables:

1. For each non-existent table:
   • Define the schema, including the partitioning field and type.
   • Create the new partitioned table in BigQuery.
   • Optionally, copy data from the existing table to the new partitioned table.

Error Handling and Logging:

1. Implement error handling to address missing tables, permission errors, or API limits.
2. Log the progress and encountered issues for monitoring purposes.

Testing and Execution:

1. Test the script thoroughly in a development environment before deployment.
2. Run the script to process tables specified in the CSV file.
   • This can be done manually or scheduled as a regular job.

This approach offers several benefits:

• Scalability: Handles partitioning of multiple tables across projects and datasets.
• Automation: Saves time and resources compared to manual partitioning.
• Flexibility: CSV format allows easy modification of table details.

Remember to:

• Use libraries compatible with BigQuery's client libraries.
• Follow Google Cloud authentication guidelines.
• Choose appropriate partitioning schemes for your data and workload.

By implementing this approach, you can efficiently manage and automate the partitioning of BigQuery tables using a simple CSV file and scripting.

[NishinThattil]

I need to do partitioning on the creation time and date but original table don't have creation time and date column but needed to be created in partitioned table.

Values in the creation time and date column should be the time data created time in the table.

[ms4446]

Since your original tables don't have a creation time and date column, you'll need to create that information during the partitioning process. Here are some refined approaches you can consider:

1. Extract Creation Time from Other Timestamps:

   • If your tables have other timestamps, like an "updated_at" column, you can use these to approximate the creation time. This might require custom logic or functions, especially if the timestamps are not consistent or straightforward.

2. Use an Ingestion Time Surrogate:

   • If the data ingestion process in BigQuery closely aligns with the actual data creation time, you can use the ingestion time as a surrogate. This approach assumes that the ingestion timestamps are consistent and accurately reflect the creation time.

3. Utilize BigQuery Metadata:

   • While BigQuery's INFORMATION_SCHEMA tables provide metadata, including the creation time of tables, they do not track the creation time of individual records. Therefore, this approach might not be feasible for partitioning based on record creation time.

4. Leverage External Data Sources:

   • If your data originates from external sources that track creation timestamps, you can integrate this information into BigQuery during the data ingestion process. This approach requires a coordinated effort to ensure the accuracy and consistency of timestamp data.

Implementation Strategies:

• In BigQuery:

  • Recreate the Table with a New Creation Time Column: Use logic (like CASE statements or custom functions) to populate a new column with the creation time, either from existing timestamps or surrogate values. Define the partition key based on this new column and automate the process using tools like the bq command-line tool or the BigQuery API.
  • Partitioned Views with Calculated Values: Create partitioned views using a WHERE clause that filters by a calculated creation time. This approach, while avoiding data movement, might not offer the same performance benefits as physical partitioning.

• In Dataflow:

  • Build a Dataflow Pipeline: Construct a pipeline that reads data from the original table, applies logic to extract or calculate the creation time, and then partitions and writes the data to a new table.

• In External Data Sources:

  • Modify Your Data Pipeline: Ensure that the creation time is captured during data extraction from the source and passed along with the data to BigQuery, using it for partitioning.

Consider factors like the availability and consistency of existing timestamps, the level of data manipulation you're comfortable with, and how well your data pipelines integrate with BigQuery.

[NishinThattil]

original table don't have other timestamps column

[ms4446]

Understood. If the original tables do not have any timestamp columns, including creation or update timestamps, you'll need to use alternative methods to approximate the creation time for partitioning. Here's a revised approach considering this constraint:

Using Ingestion Time as a Proxy for Creation Time:

Since the original data lacks timestamp columns, one practical approach is to use the ingestion time as a proxy for the creation time. BigQuery allows you to create tables partitioned by the ingestion time using the special pseudo column _PARTITIONTIME.

When you load data into BigQuery, it automatically populates the _PARTITIONTIME column with the timestamp of the data load operation. This can serve as an approximation of the creation time.

Implementing Ingestion-Time Partitioning:

1. Create a New Ingestion-Time Partitioned Table:

Use the BigQuery CREATE TABLE statement with the PARTITION BY TIMESTAMP(_PARTITIONTIME) clause to create a new table that is partitioned based on the ingestion time.

CREATE TABLE your_dataset.new_table
PARTITION BY TIMESTAMP(_PARTITIONTIME)
AS SELECT * FROM your_dataset.original_table;

2. Data Transfer:

Load your data into this new table. The _PARTITIONTIME column will be automatically populated by BigQuery.

Considerations for Ingestion-Time Partitioning:

• Data Freshness: This method assumes that the data is being loaded into BigQuery close to the time it was created. If there's a significant delay between data creation and ingestion, the _PARTITIONTIME might not accurately reflect the actual creation time.
• Backfilling Data: If you're backfilling historical data, the _PARTITIONTIME will reflect the time of the backfill, not the original creation time.

If you have external knowledge about the creation time of each record (e.g., from logs or application metadata), you could manually add a timestamp column during the data migration process.

This approach requires modifying your data loading process to include the creation timestamp for each record.