Prerequisites

• Access to a ClickHouse instance with the necessary permissions to export data.
• A Google Cloud account with billing enabled.
• Access to Google Cloud Storage and BigQuery.
• The gcloud CLI installed and configured on your local machine.
• The clickhouse-client installed on your local machine or on the server where ClickHouse is running.

1. Identify the data you want to export from ClickHouse. Make sure you have the necessary permissions to access the data.
2. Use the clickhouse-client to export the data to a CSV or TSV file. For large datasets, consider compressing the file using gzip to save space and reduce upload time.

Example command to export a table to a TSV file:

clickhouse-client --query="SELECT * FROM database.table FORMAT TSV" > data.tsv

If you want to compress the data:

clickhouse-client --query="SELECT * FROM database.table FORMAT TSV" | gzip > data.tsv.gz

1. Create a new bucket in Google Cloud Storage (GCS) or use an existing one to store the exported data file.

gsutil mb gs://your-bucket-name  


2. Upload the data file to the GCS bucket.

gsutil cp data.tsv.gz gs://your-bucket-name/path/to/data/

1. If your data is compressed, BigQuery can handle the gzip compression automatically during the load job.
2. Define the schema for your BigQuery table. You can either manually define the schema or let BigQuery attempt to auto-detect it during the import. For complex datasets, it’s generally better to define the schema explicitly.

1. Create a new dataset in BigQuery.

bq mk your_dataset_name  


2. If you’ve defined a schema, create a table with that schema in the dataset.

bq mk --table your_dataset_name.your_table_name path/to/schema.json  

1. Load the data from the GCS bucket into the BigQuery table. You can use the bq command-line tool for this.

bq load --source_format=CSV --autodetect your_dataset_name.your_table_name gs://your-bucket-name/path/to/data/data.tsv.gz  


If you have a schema file:

bq load --source_format=CSV your_dataset_name.your_table_name gs://your-bucket-name/path/to/data/data.tsv.gz path/to/schema.json  


2. Monitor the load job to ensure it completes successfully.

Once the load job is complete, run a query against the table to verify that the data has been imported correctly.

bq query --use_legacy_sql=false 'SELECT * FROM your_dataset_name.your_table_name LIMIT 10'

1. After verifying the data, you may want to clean up by removing the exported data file from the GCS bucket to avoid storage costs.

gsutil rm gs://your-bucket-name/path/to/data/data.tsv.gz  


2. If you created a new dataset and table specifically for this import and no longer need them, you can remove those as well.

bq rm -r -f your_dataset_name  

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Notes:

• This guide assumes that you are familiar with the command-line tools for ClickHouse, Google Cloud Storage, and BigQuery (clickhouse-client, gsutil, and bq, respectively).
• Always ensure that your data is backed up and securely handled throughout this process.
• For large datasets, consider using batch operations and monitoring to manage the data transfer efficiently.
• You may need to adjust the field delimiter and other format options in the bq load command depending on the format of your exported data file.
• If you encounter errors during the BigQuery load job, check the error messages and adjust your schema or data file accordingly.