## Upcoming events
## 24.3 release  The big feature in the 24.3 release is the analyzer being enabled by default. Analyzer is a new query analysis and optimization infrastructure that’s been in the works for a couple of years and lets you have multiple ARRAY JOIN clauses in a query, treats tuple elements like columns, handles queries with nested CTEs and sub-queries, and more. Read the release post
## Storing Continuous Profiling Data in ClickHouse  Coroot is an open-source tool for observability that turns observability data into actionable insights. Nikolay Sivko wrote a blog post in which he describes how they built their own storage system for profiling data based on ClickHouse. After defining continuous profiling, Nikolay takes us through the data model and gives examples of queries that check on the performance of a service. Read the blog post
## Migrating to ClickHouse: Releem's Journey Releem is a MySQL performance tuning tool that automatically detects performance degradation and optimizes configuration files. To do this, they collect metrics from hundreds of database servers across various operating systems and cloud solutions. They used to store these metrics in MySQL, which started to struggle once it reached almost 5 billion records. Enter ClickHouse, which helped shrink the database size by 20 times, cut aggregation query times from 45 to 2 minutes, and reduced the page load time of the Releem dashboard by 25%. Read the blog post
## How we Built a 19 PiB Logging Platform with ClickHouse and Saved Millions  Rory Crispin, SRE at ClickHouse, shared his experience building a platform for the logging data generated by ClickHouse Cloud. Rory takes us through key design decisions, including whether to use Kafka and structured vs unstructured logging. He also explains why the team decided to use OpenTelemetry to collect metrics and does a cost comparison of the in-house solution vs using an off-the-shelf product like Datadog. Read the blog post
## Building a Rate Limiter with ClickHouse  If you were going to build a rate limiter, the obvious choice for storing the data would be Redis. But Brad Lhotsky, Systems and Security Administrator at Craigslist, was curious whether ClickHouse would be fit-for-purpose and used it to build a proof-of-concept. Brad shared the slides of a talk explaining how he imported data from Kafka, built a bridge from the ACL API to ClickHouse, and tested high availability, all in just one week. View the slide deck
## Video corner
## ClickHouse Cloud Updates 
## Post of the month Our favorite post this month was by Divyendu Singh about real-time monitoring.  See it here