Building an Observability platform with ClickHouse

Aug 28, 20211 like763 views

The document discusses using ClickHouse as the backend storage for observability data in SigNoz, an open source observability platform. ClickHouse is well-suited for storing observability data due to its ability to handle wide tables, perform fast aggregation queries, and provide compression capabilities. The document outlines SigNoz's architecture, demonstrates how tracing and metrics data is modeled in ClickHouse, and shows how ClickHouse outperforms Elasticsearch for ingesting and querying logs and metrics at scale. Overall, ClickHouse is presented as an efficient and less resource-intensive solution than alternatives like Druid for storing observability data, especially for open source projects.

Building an Observability
platform with ClickHouse
Ankit Nayan (CTO, SigNoz)
https://github.com/SigNoz/signoz

About Me
- Building SigNoz, part of YCombinator
- 8 yrs experience in build scalable
tech architecture
- Passionate about distributed systems
- Love Himalayas and trekking

SigNoz - Open source observability platform
👉 Metrics + Traces in a single pane
👉 Powerful trace filtering and aggregation capabilities
👉 Out-of-box like SaaS solutions, requiring minimal dev
effort
👉 Native backend and frontend to OpenTelemetry
👉 Can be run within your own cloud
https://github.com/SigNoz/signoz

Quick Demo
https://github.com/SigNoz/signoz

Architecture of SigNoz
Components
● OpenTelemetry Collector
● ClickHouse
● Query Service
● Frontend
https://github.com/SigNoz/signoz

Why ClickHouse for storing Observability data
● Wide tables with each query processes large number of
rows but a few columns
● Running aggregate queries
● Distributed columnar datastore and blazing fast queries
● Compression capabilities
● Hot and cold storage

Tracing data model in ClickHouse
Using Array(String) to
store tagKeys and
tagValues
Extracting frequently used
tags to columns
https://github.com/jaegertrac
ing/jaeger/issues/1438

Storage Performance of Clickhouse
https://github.com/SigNoz/signoz

ClickHouse for Logs Management at Uber
[1] https://eng.uber.com/logging/

Comparison with Elastic
● A single ClickHouse node could
ingest 300K logs per second, 10x
a single ES node
● >80% queries are aggregation
queries, such as terms, histogram
and percentile aggregations.
Elastic is not designed to support
fast aggregation
[1] https://eng.uber.com/logging/

$Metrics Data model for ClickHouse {"__name__":"up","instance":"clickhouse_exporter_1:91 16","job":"clickhouse"} │$

Metrics @ SigNoz
● Metrics with ClickHouse uses 1.16 byte vs 1.37
byte with Prom local storage
● PromQL like query model for reading metrics data
from ClickHouse
https://github.com/SigNoz/signoz

Planned Improvements
● Another table to store complete span and index by traceID
● Use ReplicatedMergeTree for data redundancy
● Use SummingMergeTree for faster aggregation queries
● Use distributed tables for distributed queries
● Separation of roles using separate data and query nodes
leads to independent scaling and independent hardware
SKUs
https://github.com/SigNoz/signoz

Summary
● ClickHouse seems a good fit for Observability data
● Uber has used ClickHouse at scale for logs
● Aggregation of span data more efficient with OLAP DBs
● Less resource intensive for smaller scale compared to
Druid, ideal for open source project like ours
https://github.com/SigNoz/signoz

Thank You
ankit@signoz.io
@ankitnayan
https://github.com/SigNoz/signoz
Give SigNoz a try. Check out our Github repo at

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This is a joint webinar Percona - Altinity. In this webinar we will discuss suggestions and tips on how to recognize when MySQL is overburdened with analytics and can benefit from ClickHouse’s unique capabilities. We will then walk through important patterns for integrating MySQL and ClickHouse which will enable the building of powerful and cost-efficient applications that leverage the strengths of both databases.

Fun with ClickHouse Window Functions-2021-08-19.pdfAltinity Ltd

Fun with ClickHouse Window Functions | Altinity Webinar Window functions have arrived in ClickHouse! Our webinar will start with an introduction to standard window function syntax and show how it is implemented in ClickHouse. We’ll next show you problems that you can now solve easily using window functions. Finally, we’ll compare window functions to arrays, another powerful ClickHouse feature. There will be time for questions with our SQL experts. Join us for a complete overview of this long-awaited feature! Speakers: Robert Hodges, CEO @Altinity Vitaliy Zakaznikov, QA Manager and Architect @Altinity

Cloud Native Data Warehouses - Intro to ClickHouse on Kubernetes-2021-07.pdfAltinity Ltd

Robert Hodges from Altinity, an enterprise provider of ClickHouse and developer of the ClickHouse Kubernetes operator, provides an introduction to running ClickHouse on Kubernetes. The presentation demonstrates how to deploy ClickHouse and Zookeeper on Kubernetes using the ClickHouse Kubernetes operator. It shows how to define ClickHouse installations using custom resources, access ClickHouse, and update the cluster configuration, such as changing the number of shards and replicas or ClickHouse version. The operator automatically applies configuration changes to pods.

Building High Performance Apps with Altinity Stable Builds for ClickHouse | A...Altinity Ltd

Altinity Stable Builds offer a ClickHouse distribution that is ready for production use and with 3 years of maintenance. Our webinar introduces the special features of Stable Builds and describes how we build them from ClickHouse Long-Term Support (LTS) releases. We’ll show you how to find them and install them yourself, then guide you through the important topic of upgrading. We’ll also walk through how to use Altinity Stable Builds in Altinity.Cloud, our managed ClickHouse platform for high-performance analytics.

Own your ClickHouse data with Altinity.Cloud Anywhere-2023-01-17.pdfAltinity Ltd

The document discusses Altinity.Cloud Anywhere, a service that allows users to run ClickHouse databases on their own Kubernetes clusters. It provides automation of ClickHouse operations and management through the Altinity Connector. Users can prepare their own Kubernetes environment, connect it to Altinity.Cloud, and then launch and manage ClickHouse clusters on their infrastructure. Advanced topics covered include how the service works internally and how to get support from Altinity.

ClickHouse ReplacingMergeTree in Telecom AppsAltinity Ltd

Adventures with the ClickHouse ReplacingMergeTree EngineAltinity Ltd

Building a Real-Time Analytics Application with Apache Pulsar and Apache PinotAltinity Ltd

This document provides an overview of building a real-time analytics application with Apache Pulsar and Apache Pinot. It introduces Mary Grygleski and Mark Needham, describes what real-time analytics is, and discusses the properties of real-time analytics systems. It then demonstrates how to ingest data from the Wikimedia recent changes feed into Pulsar and Pinot for real-time analytics and builds a dashboard with the data using Streamlit.

Altinity Webinar: Introduction to Altinity.Cloud-Platform for Real-Time Data.pdfAltinity Ltd

Altinity Webinar: Introduction to Altinity.Cloud-Platform for Real-Time Data - Presentation Slides Altinity.Cloud is a fully automated cloud service for ClickHouse that is optimized for real-time analytics. In this webinar, we’ll explain how Altinity.Cloud works, then show how to set up your first ClickHouse cluster. We’ll then tour important features like scale-up, scale-out, uptime schedules, and DBA tools to analyze your tables. You’ll learn everything necessary to start working on real-time analytics today. Bring your questions! Presenters: Robert Hodges & Alexander Zaitsev Note: This webinar will be recorded and later posted on our Webinar page (https://altinity.com/webinarspage/) or Altinity official Youtube channel (https://www.youtube.com/@Altinity).

OSA Con 2022 - What Data Engineering Can Learn from Frontend Engineering - Pe...Altinity Ltd

OSA Con 2022 - Welcome to OSA CON Version 2022 - Robert Hodges - Altinity.pdfAltinity Ltd

OSA Con 2022 - Using ClickHouse Database to Power Analytics and Customer Enga...Altinity Ltd

OSA Con 2022: Using ClickHouse Database to Power Analytics and Customer Engagement Platform Prafulla Gupta - Times Internet This talk covers how we empowered Product Managers and Editors at Times Internet by developing an in-house product, GrowthRx, using Clickhouse Open Source Database to track and analyze user behavior to increase user retention and customer engagement. Times Internet is India's largest digital news publisher, which manages leading brands like Times of India, Economic Times, Navbharat Times, etc, where we are tracking more than 10 billion events per month in the ClickHouse Database.

OSA Con 2022 - Tips and Tricks to Keep Your Queries under 100ms with ClickHou...Altinity Ltd

- ClickHouse can query 170 billion rows at 500 queries per second with a 99th percentile latency of 110ms through careful data modeling, query optimization, and use of materialized views. - To achieve low latency at high query rates, it is important to reduce the amount of data scanned by queries through techniques like sorting keys, data compression, and reducing data cardinality. - Materialized views can reduce data sizes by 1000-10,000x and are critical for maintaining low query latencies on large datasets. Dividing data into read and write replicas also improves query performance.

OSA Con 2022 - The Open Source Analytic Universe, Version 2022 - Robert Hodge...Altinity Ltd

OSA Con 2022 - Switching Jaeger Distributed Tracing to ClickHouse to Enable A...Altinity Ltd

The document discusses how OpsVerse migrated their Jaeger distributed tracing storage from Cassandra to ClickHouse for improved performance monitoring. Jaeger is an open source distributed tracing system that was originally designed to use Elasticsearch or Cassandra for storage. While Cassandra worked well for basic functionality, it lacked capabilities for advanced analytics. ClickHouse supports richer query functions and better handles large datasets. The document outlines the steps OpsVerse took to implement the ClickHouse storage plugin for Jaeger and deploy ClickHouse on Kubernetes using the ClickHouse Operator. This migration enabled more insightful performance monitoring and analytics.

OSA Con 2022 - Streaming Data Made Easy - Tim Spann & David Kjerrumgaard - St...Altinity Ltd

OSA Con 2022 - State of Open Source Databases - Peter Zaitsev - Percona.pdfAltinity Ltd

OSA Con 2022 - State of Open Source Databases Peter Zaitsev - Percona It has been an exciting year in the open-source database industry, with more choices, more cloud, and key changes in the industry. We will dive into the key developments over 2022, including the most important open-source database software releases in general, the significance of cloud-native solutions in a multi-vendor multi-cloud world, the new criticality of security challenges, and the evolution of the open-source software industry.

OSA Con 2022 - Specifics of data analysis in Time Series Databases - Roman Kh...Altinity Ltd