Streamlining CSV Data Ingestion in SingleStore
Vishwajeet Dabholkar
Solutions Engineer| Prompt Engineer| GenAI | Vectors DBs | RAG Applications | LLM applications | Data Engineer | Data Streaming | RAG Expert |
Hello all,
Today, I'd like to discuss a common challenge encountered during CSV data ingestion in SingleStore, and more importantly, share a solution to overcome it.
Problem:
Often, when attempting to ingest CSV data into a SingleStore structured table via a pipeline and stored procedure, we encounter an error: "Row 1 doesn't contain data for all columns". This typically arises when the number of columns in the ingested CSV file and the target table do not match, resulting in data misalignment during the ingestion process.
Consider the target table temp, with a set of defined columns including a source_file column intended to store the name of the CSV file from which the data is ingested.
CREATE TABLE temp(
????col1 VARCHAR(50),
????col2 VARCHAR(50),
????col3 VARCHAR(50),
????col4 VARCHAR(50),
????source_file VARCHAR(1000),
????SHARD KEY(col1)
);
The error could surface from the stored procedure where a "SELECT *" statement is used, intending to select all columns from the ingested batch of data.
DELIMITER //
CREATE OR REPLACE PROCEDURE sp_temp (batch QUERY(col1 VARCHAR(50), col2 VARCHAR(50), col3 VARCHAR(50), col4 VARCHAR(50), source_file VARCHAR(1000)))
AS
BEGIN
????INSERT INTO temp (col1, col2, col3, col4, source_file)
????????SELECT *
????????FROM batch;
END //
DELIMITER ;
Solution:
To resolve this, we need to modify the stored procedure, specifically the SELECT clause. Instead of "SELECT *", we specify the columns individually. This ensures proper alignment between the ingested data and the target table structure.
DELIMITER //
CREATE OR REPLACE PROCEDURE sp_temp (batch QUERY(col1 VARCHAR(50), col2 VARCHAR(50), col3 VARCHAR(50), col4 VARCHAR(50), source_file VARCHAR(1000)))
AS
BEGIN
????INSERT INTO temp (col1, col2, col3, col4, source_file)
????????SELECT col1, col2, col3, col4, source_file
????????FROM batch;
END //
DELIMITER ;
Additionally, we need to revise the pipeline DDL statement. Here, we must accurately match the number of columns in the CSV files and set the source_file column with the pipeline_source_file() function.
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CREATE OR REPLACE PIPELINE sp_temp AS
LOAD DATA LINK 'sds2' 'data_folder/csv_data/'
INTO PROCEDURE sp_temp
(col1, col2, col3, col4) -- columns in file
COLUMNS TERMINATED BY ','
OPTIONALLY ENCLOSED BY '"'
IGNORE 1 LINES
SET source_file = pipeline_source_file(); -- column which we want to set in the table
These changes ensure accurate ingestion of the data from source CSV files into the SingleStore target table, maintaining both the data integrity and the source file information for future reference.
While the solution we discussed provides a clear path to tackling a common ingestion problem, it's worth discussing why this approach can be advantageous compared to custom Python or PySpark jobs for data ingestion.
Simplicity: Writing SQL procedures and pipelines in SingleStore is much simpler than developing custom ingestion jobs using Python or PySpark. This drastically reduces the complexity of your codebase, making it more manageable, maintainable, and less prone to bugs.
Real-time Ingestion: SingleStore's pipelines enable near real-time data ingestion from a variety of sources. Achieving this level of latency with custom Python or PySpark jobs can be more challenging and resource-intensive.
Automation: Once set up, SingleStore pipelines automatically manage data ingestion, removing the need for manual intervention. In comparison, custom Python or PySpark jobs often require constant monitoring and manual triggering.
Scalability: SingleStore pipelines are designed to work seamlessly in distributed environments. This allows for high throughput and easy scalability. On the other hand, scaling custom Python or PySpark jobs can be a complex task requiring additional engineering work.
Error Handling: SingleStore provides built-in error handling and retry mechanisms. Implementing these features in custom Python or PySpark jobs would require writing additional code.
Transaction Control: SingleStore ensures atomicity, consistency, isolation, and durability (ACID) at the transaction level. Ensuring these properties with custom jobs would require significant effort and could introduce complexity.
Utilizing SingleStore pipelines for data ingestion can significantly streamline the process and improve performance. This allows data engineers to focus more on extracting valuable insights from data, rather than struggling with ingestion issues.
As data engineers, we should always strive to leverage the best tools and practices available to us. SingleStore pipelines clearly offer a range of benefits that can make our lives easier. So next time you are faced with an ingestion problem, why not give SingleStore a try?
Until next time, keep coding!