ClickHouse DBA Script Archives | Page 8 of 10

Leveraging ClickHouse for Real-Time Predictive Analytics

Introduction Predictive analytics solutions require fast and scalable storage solutions that can handle large amounts of data and support real-time analysis. ClickHouse is a columnar database management system optimized for OLAP (Online Analytical Processing) workloads […]

Monitoring ClickHouse Query Parser Performance

Shiv Iyer

Introduction To monitor the Parser performance in ClickHouse, you can use the system.query_log system table, which contains information about all queries executed in the cluster. Here’s an example SQL code that you can use to […]

How does ClickHouse Resource Governor work?

Shiv Iyer

Introduction to ClickHouse Resource Governor ClickHouse Resource Governor is a feature that allows DBAs to allocate system resources, such as CPU, memory, and I/O operations, to specific users or groups of users. The Resource Governor […]

ClickHouse Troubleshooting: Avoiding Excessive Disk Swaps

Shiv Iyer

Introduction Disk swapping is a technique used by operating systems to manage memory usage when the amount of available physical memory is limited. When the operating system runs out of available physical memory, it uses […]

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 […]

ClickHouse Troubleshooting: How to Identify Blocks Causing Latch Contention

Shiv Iyer

Introduction Latch contention in ClickHouse can have a significant impact on performance. A latch is a synchronization mechanism that allows multiple threads to access a shared resource, such as a data block, in a controlled […]

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 […]

* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from ChistaDATA Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google, You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding.

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