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Ensure both the source and destination ClickHouse instances are set up and accessible. Verify that you have the necessary permissions to read data from the source and write to the destination. Make sure both ClickHouse servers are running compatible versions to prevent any compatibility issues.
Determine which tables and databases need to be transferred. Document the schema and any dependencies, such as views or materialized views. This step is crucial for ensuring that all necessary data is moved and that the schema will be correctly recreated on the destination.
Use ClickHouse's native `CLICKHOUSE` command-line client to export data. You can use the `SELECT ... INTO OUTFILE` syntax to export data from the source database into a CSV or TSV file. For example:
```bash
clickhouse-client --host= --query="SELECT * FROM .
" --format=TSV > /path/to/exported_data.tsv
```
Ensure the exported file is stored in a location accessible for transfer.
Use secure file transfer methods such as `scp` or `rsync` to transfer the exported data files from the source server to the destination server. Ensure file permissions are set correctly to allow reading by the ClickHouse process on the destination server.
```bash
scp /path/to/exported_data.tsv user@destination_host:/path/to/destination/
```
Before importing data, recreate the database schema on the destination ClickHouse instance. Use the `SHOW CREATE TABLE` command on the source to get the schema definition and execute it on the destination. This ensures that tables are created with the correct structure.
```bash
clickhouse-client --host= --query="SHOW CREATE TABLE .
"
# Execute the output on the destination server.
```
Use the `clickhouse-client` to import the exported data into the corresponding tables on the destination server using the `INSERT INTO ... FORMAT` syntax. For example:
```bash
clickhouse-client --host= --query="INSERT INTO .
FORMAT TSV" < /path/to/destination/exported_data.tsv
```
Ensure the data formats match and that any necessary transformations are applied during the import.
After the data has been imported, perform integrity checks to ensure that the data was transferred correctly. Compare row counts and checksums between the source and destination to verify consistency. Use queries like:
```bash
clickhouse-client --host= --query="SELECT count(*) FROM .
"
clickhouse-client --host= --query="SELECT count(*) FROM ."
```
This step ensures the data transfer was successful and complete.
By following these steps, you can effectively transfer data between ClickHouse instances without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
An open-source database management system for online analytical processing (OLAP), ClickHouse takes the innovative approach of using a column-based database. It is easy to use right out of the box and is touted as being hardware efficient, extremely reliable, linearly scalable, and “blazing fast”—between 100-1,000x faster than traditional databases that write rows of data to the disk—allowing analytical data reports to be generated in real-time.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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