While AWS CloudWatch is a good service for basic monitoring and alerts, on its own it may not be the best solution for log data at scale. Common user interface and scalability issues can hold users back from leveraging Amazon CloudWatch logs for troubleshooting use cases.
Whether cloud infrastructure logs across AWS services, container logs for Lambda functions, security telemetry data, or network device logs, CloudWatch can become unwieldy under the weight of non-stop log streams. CloudOps, DevOps, SecOps, and business users demand better access to more logs for longer periods of time, which requires a dedicated log analytics solution to complement CloudWatch metrics monitoring.
Amazon CloudWatch is well-integrated with AWS services, making it relatively easy to monitor real-time application performance and key metrics, as well as create alarms. However, if you use multiple public cloud computing services or on-premises services, CloudWatch is not effective as a monitoring tool.
When it comes to troubleshooting and root cause analysis, the complex UI for CloudWatch gets confusing. Once organizations amass a high enough volume of logs, filtering and searching in the CloudWatch interface becomes far too complicated. Finding the root cause of an error involves scrolling manually through pages and pages of CloudWatch log groups to locate the specific invocation that threw an error.
Even then, the data points surfaced may not be enough to troubleshoot with – CloudWatch lacks the data integration depth and correlation features necessary to recognize very complex patterns or perform root cause analysis across larger and multiple data sources.
Even if all of your workloads run in AWS, you may not be able to collect and analyze as much data from them as you would like when using Amazon CloudWatch. CloudWatch only supports specific predefined log data and metrics types. Even then, retention at scale becomes too costly.
Querying and scaling data isn’t the best use case for CloudWatch. Once teams reach terabyte scale (and need log retention beyond a short period of time such as a few days or a week), CloudWatch simply becomes impractical and difficult to use. This is especially true if you need a longer retention period for compliance reasons, or to tap into the value of long-term log storage for security use cases, forensics, or customer and product analytics.
For those reasons, choose a log analytics strategy that gives you the flexibility to store your data anywhere, and for the long run. Even if you use CloudWatch to collect data initially, unlock additional value by storing all data centrally in Amazon S3 and enabling analytics with a more powerful platform like ChaosSearch.
Amazon CloudWatch dashboards can help you visualize your log data, as well as monitor your resources and applications with CloudWatch alarms. But, these monitoring tools are not very useful if you need to search through the data or run complex queries on it. To do these things, you’ll need to extend your log analytics strategy with other tools that support sophisticated log queries and allow you to parse multiple logs at once.
CloudWatch users can leverage ChaosSearch by exporting CloudWatch logs, moving them to S3 and indexing all log data stored in S3. You may choose to bypass CloudWatch altogether and push logs directly into a more comprehensive and powerful analytics platform. This strategy is also effective as a log analysis complement for Datadog users.
ChaosSearch allows you to index any data stored in S3 s in the log, JSON, or CSV format. There is a vast ecosystem of log shippers and tools to transport data to cloud object storage (Amazon S3) from Logstash and beats, Fluentd, Fluentbit to Vector, Segment.io, Cribl.io or programmatically from Boto3. From there, you can take advantage of powerful log analytics in the cloud with unlimited data retention – without some of the complexity and cost unpredictability issues that come with CloudWatch.