Navigating the Data Landscape: A Journey from PostgreSQL to ClickHouse
September 1, 2024, 5:23 am
In the fast-paced world of data engineering, the choice of database can make or break a project. This is a tale of a small team that embarked on a significant migration journey. They transitioned from PostgreSQL to ClickHouse, seeking speed and efficiency. The road was bumpy, filled with lessons learned and pitfalls encountered. Here’s how they navigated the data landscape.
The story begins with a common challenge: performance. The team at Okko found themselves grappling with slow analytical queries. An average query took about a minute to return results. This sluggishness was unacceptable in a data-driven environment. They needed a solution that could keep pace with their growing demands. Enter ClickHouse, a columnar database designed for online analytical processing (OLAP).
The migration process was akin to moving houses. They had to pack up their existing data, transport it, and set it up in a new environment. The team consisted of just a few data engineers and a larger group of analysts. The stakes were high, and the timeline was tight. They dove headfirst into the migration, often without fully understanding the nuances of ClickHouse.
One of the first missteps was the unchecked use of the ReplacingMergeTree engine. This engine seemed like a silver bullet, promising easy updates and deduplication. However, the team soon discovered that relying on it for all their data storage needs was a mistake. The engine automatically merges data in the background, but this process can be resource-intensive. When dealing with billions of rows, the operation became a bottleneck.
Imagine trying to clean a massive warehouse. If you only focus on one corner, the rest of the space remains cluttered. Similarly, the team learned that they needed to manage their data more strategically. Instead of forcing merges on large datasets, they could drop partitions and insert new data. This approach was like sweeping the entire warehouse clean, rather than tidying up one section at a time.
The team also faced challenges with the ALTER DELETE command. In their previous experience with other databases, this command was a go-to for removing unwanted data. However, in ClickHouse, it turned out to be a heavyweight operation. It involved rewriting large chunks of data, which was inefficient. Instead, they could have simply dropped entire partitions, a much lighter operation.
The importance of partitioning became clear as they delved deeper into ClickHouse. Proper partitioning allowed them to manage data more effectively. It was like organizing files in a cabinet—everything had its place, making retrieval faster and easier. The team learned to partition their data by time, which aligned with their analytical needs.
Another lesson came from the need to educate their analysts. The team realized that they hadn’t communicated the intricacies of ClickHouse’s sorting and partitioning features. Analysts were running queries without understanding how data was organized. This oversight led to inefficient queries that scanned entire tables instead of leveraging the power of indexed data.
In the world of databases, knowledge is power. The team began to hold training sessions, sharing insights about how to optimize queries. They emphasized the importance of understanding primary keys and how ClickHouse uses them to speed up searches. This shift in approach transformed the way analysts interacted with the data.
As the migration progressed, the team also discovered the value of documentation. Each lesson learned was meticulously recorded. This practice became a roadmap for future projects. New team members could quickly get up to speed, avoiding the same pitfalls.
The journey from PostgreSQL to ClickHouse was not just about technology; it was about culture. The team learned to embrace a mindset of continuous improvement. They became more agile, adapting their strategies based on real-time feedback. This adaptability was crucial in a landscape that is constantly evolving.
In conclusion, the migration to ClickHouse was a transformative experience for the team at Okko. They faced challenges head-on, learning valuable lessons along the way. By embracing the unique features of ClickHouse, they improved their data processing capabilities significantly. The average query time dropped from a minute to just a couple of seconds.
This journey serves as a reminder that in the world of data, the right tools and knowledge can lead to remarkable results. As teams continue to navigate the complexities of data engineering, the lessons learned from this migration will resonate. It’s not just about the technology; it’s about the people behind it, their willingness to learn, adapt, and grow.
In the end, the road may be long, but with each step, the destination becomes clearer. The landscape of data is vast, and with the right approach, teams can thrive in it.
The story begins with a common challenge: performance. The team at Okko found themselves grappling with slow analytical queries. An average query took about a minute to return results. This sluggishness was unacceptable in a data-driven environment. They needed a solution that could keep pace with their growing demands. Enter ClickHouse, a columnar database designed for online analytical processing (OLAP).
The migration process was akin to moving houses. They had to pack up their existing data, transport it, and set it up in a new environment. The team consisted of just a few data engineers and a larger group of analysts. The stakes were high, and the timeline was tight. They dove headfirst into the migration, often without fully understanding the nuances of ClickHouse.
One of the first missteps was the unchecked use of the ReplacingMergeTree engine. This engine seemed like a silver bullet, promising easy updates and deduplication. However, the team soon discovered that relying on it for all their data storage needs was a mistake. The engine automatically merges data in the background, but this process can be resource-intensive. When dealing with billions of rows, the operation became a bottleneck.
Imagine trying to clean a massive warehouse. If you only focus on one corner, the rest of the space remains cluttered. Similarly, the team learned that they needed to manage their data more strategically. Instead of forcing merges on large datasets, they could drop partitions and insert new data. This approach was like sweeping the entire warehouse clean, rather than tidying up one section at a time.
The team also faced challenges with the ALTER DELETE command. In their previous experience with other databases, this command was a go-to for removing unwanted data. However, in ClickHouse, it turned out to be a heavyweight operation. It involved rewriting large chunks of data, which was inefficient. Instead, they could have simply dropped entire partitions, a much lighter operation.
The importance of partitioning became clear as they delved deeper into ClickHouse. Proper partitioning allowed them to manage data more effectively. It was like organizing files in a cabinet—everything had its place, making retrieval faster and easier. The team learned to partition their data by time, which aligned with their analytical needs.
Another lesson came from the need to educate their analysts. The team realized that they hadn’t communicated the intricacies of ClickHouse’s sorting and partitioning features. Analysts were running queries without understanding how data was organized. This oversight led to inefficient queries that scanned entire tables instead of leveraging the power of indexed data.
In the world of databases, knowledge is power. The team began to hold training sessions, sharing insights about how to optimize queries. They emphasized the importance of understanding primary keys and how ClickHouse uses them to speed up searches. This shift in approach transformed the way analysts interacted with the data.
As the migration progressed, the team also discovered the value of documentation. Each lesson learned was meticulously recorded. This practice became a roadmap for future projects. New team members could quickly get up to speed, avoiding the same pitfalls.
The journey from PostgreSQL to ClickHouse was not just about technology; it was about culture. The team learned to embrace a mindset of continuous improvement. They became more agile, adapting their strategies based on real-time feedback. This adaptability was crucial in a landscape that is constantly evolving.
In conclusion, the migration to ClickHouse was a transformative experience for the team at Okko. They faced challenges head-on, learning valuable lessons along the way. By embracing the unique features of ClickHouse, they improved their data processing capabilities significantly. The average query time dropped from a minute to just a couple of seconds.
This journey serves as a reminder that in the world of data, the right tools and knowledge can lead to remarkable results. As teams continue to navigate the complexities of data engineering, the lessons learned from this migration will resonate. It’s not just about the technology; it’s about the people behind it, their willingness to learn, adapt, and grow.
In the end, the road may be long, but with each step, the destination becomes clearer. The landscape of data is vast, and with the right approach, teams can thrive in it.