Скачать или смотреть Improving Pentaho Performance with PostgreSQL: Tips for Faster Data Throughput

Discover how to enhance `Pentaho` performance when working with `PostgreSQL` tables. Learn about the differences between table input and database lookup methods for optimal results.
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Improving Pentaho Performance with PostgreSQL: Tips for Faster Data Throughput

When working with large datasets in Pentaho, performance can sometimes be an issue, especially when retrieving data from a database like PostgreSQL. A common question from users is: Why is my Pentaho table input taking so long to read data from PostgreSQL, even when querying only a couple of columns? In this blog, we will explore the challenges around data throughput in Pentaho and provide solutions to enhance performance.

Understanding the Problem

In this particular case, a user experiences poor performance while attempting to read from a PostgreSQL table using Pentaho. Despite querying only three columns from a table that contains 20 columns and approximately 4.8 million rows, the system processes an average of only 7,632 lines per second. This is significantly below the desired 15,000 rows per second throughput. Below is a summary of the problem:

Database: PostgreSQL

Table: individuals with 20 columns and ~4.8 million rows

Pentaho Version: 7.1

Input Query:

[[See Video to Reveal this Text or Code Snippet]]

Performance Log:

Table input processed 4,869,591 lines at 7,632 lines/s

Stream lookup processed 9,754,378 lines at 15,288 lines/s

Given these statistics, it's clear that there is room for improvement. So, how can we increase this data retrieval performance?

Solutions for Enhancing Performance

1. Differentiate Between Input Methods

One crucial piece of advice derived from user experience is recognizing the difference in performance between Table Input and Database Lookup in Pentaho. Here’s a breakdown:

Table Input: This method results in slower data processing due to the way it handles data streams. It reads each row one by one from the database which can impede throughput.

Database Lookup: In contrast, this method directly retrieves the relevant data from the database, significantly improving performance for data retrieval tasks.

2. Optimize Queries

While the initial query to fetch data looks simple, there are ways to enhance it further:

Limit Columns: Always limit the number of columns in queries to only those necessary. This reduces the amount of data transferred and processed.

Introduce Filtering: If possible, add WHERE clauses to minimize the rows being fetched—this will drastically reduce the data size and time taken.

3. Review Server Configuration

When deploying Pentaho on a server like Oracle Linux, ensuring adequate resources is vital:

RAM: Ensure there is enough RAM available. The current server has 65,707 MB of RAM which is robust, but it's beneficial to monitor usage closely.

SQL Connection Settings: Adjust JDBC settings on your PostgreSQL connection to achieve better connectivity and throughput.

4. Use Efficient Steps in Data Integration

Not all steps in Pentaho provide the same performance. Be strategic about what steps you select for your ETL (Extract, Transform, Load) processes:

Test Different Approaches: Users have reported that switching from Table Input to Database Lookup has induced better performance metrics under similar load conditions.

Use Dummy Steps for Testing: Before deploying significant changes, employ dummy steps to simulate different configurations and measure their impact on performance.

Conclusion

When experiencing sluggish performance with Pentaho and PostgreSQL, consider changing your data input method from Table Input to Database Lookup. This change, along with optimized queries and suitable server configurations, can significantly enhance your data processing throughput. Remember, fine-tuning not only affects how fast you can read the data