ClickHouse Archives | Page 30 of 42

ClickHouse EXPLAIN: Determine JOIN Order in Query Execution Plan

Introduction In ClickHouse, the join order in an execution plan is determined by the query optimizer, which analyzes the query and generates an optimal plan for executing the query. The optimizer uses statistics about the […]

Using TOP Command to Measure ClickHouse Server Load & Queue Length

Shiv Iyer

Introduction Measuring load and queue length of ClickHouse Server is important for troubleshooting performance bottleneck. The load is a measure of the amount of work that ClickHouse is currently doing or is capable of doing. […]

How to Monitor & Troubleshoot Log Contention in ClickHouse

Shiv Iyer

Introduction Log contention in ClickHouse occurs when multiple write operations compete for the same log file, causing performance issues and potentially leading to data corruption. Here are some steps you can take to troubleshoot and […]

Monitoring Query Latency due to Wait and Latch Events in ClickHouse

Shiv Iyer

Introduction In ClickHouse, you can monitor query latency and waits/latches using the system.query_log and system.metrics tables. Here is an example query that can be used to monitor query latency and wait/latch events: This query selects […]

How NULL Values affect ClickHouse Query Performance

ClickHouse Troubleshooting: How NULL Values affect Query Performance

Shiv Iyer

Introduction NULL values in ClickHouse can affect performance in several ways. One way is that they can increase the size of data, which can lead to slower query execution times as more data needs to […]

Streaming ClickHouse Data to Kafka

ChistaDATA Inc.

Introduction ClickHouse has an inbuilt Kafka table engine which is commonly used to read streaming messages from Apache Kafka and store it in ClickHouse. This is one of the important and widely used features of […]

ClickHouse Live Views to Compute Real-time Moving Averages

ClickHouse Materialized Views: Using Live Views to Compute Real-time Moving Averages

Shiv Iyer

Introduction ClickHouse Live Views can be used to compute real-time moving averages efficiently. Here’s how: Conclusion By using ClickHouse Live Views and defining the appropriate query, settings, and subscription, you can efficiently compute real-time moving […]

ClickHouse Performance: Implementing Dynamic Disks in ClickHouse 23.2

Shiv Iyer

Introduction Dynamic disks in ClickHouse 23.2 refer to a feature that allows for more efficient use of disk space and improved performance when working with large amounts of data. Traditionally, ClickHouse used a file-based storage […]

Fatal Background Processes in ClickHouse

Shiv Iyer

Introduction ClickHouse is an open-source columnar database management system that is designed to handle high volumes of data and support high concurrency. However, like any complex system, it is possible for background processes to fail, […]

How does ClickHouse Merge Sort Algorithm work?

Shiv Iyer

Introduction ClickHouse uses a merge-sort algorithm to sort data internally. The merge-sort algorithm works by dividing the data into smaller blocks, sorting each block individually, and then merging the sorted blocks together to produce the […]

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