Elasticsearch Replacement for Log Analytics at Scale
Elasticsearch Cannot Scale to Meet Today’s Log Management Challenge
While the Elasticsearch stack is free open-source software and is easy to download and install, today’s exponential growth in log volume has exposed Elasticsearch’s deficiencies for managing logs at scale. Whether deployed on-premises or as a cloud service, an Elasticsearch cluster requires actively managing the underlying compute and storage infrastructure to meet log volume growth and growing demand from end users for more log data and faster queries on dynamic data sets.
Whether cloud infrastructure logs, container logs, security telemetry data, or network device logs, Elasticsearch is bending and breaking under the weight of non-stop log volume growth. CloudOps, DevOps, SecOps, and business users will only demand better access to more logs for longer periods of time which will continue exposing the shortcomings of Elasticsearch at scale:
A BETTER APPROACH TO LOG ANALYTICS
Imagine sending all your data to your cloud environment in its native format—no parsing or schema changes. ChaosSearch indexes all data as-is, without transformation, while auto detecting native schemas.
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Management Complexity Consumes Resources
An Elasticsearch cluster—whether managed Elasticsearch in the cloud or on-premises— becomes brittle at scale and requires active, hands-on administration. Compute and storage are tightly coupled resulting in cost spikes or failures when log volumes rise (which they always do).
TCO Increases Dramatically at Scale
The true cost of an Elasticsearch cluster must include the cost of administration and maintenance. And the inefficient indexing technology of the Lucene database can swell data size. Admins are often forced to make trade-offs—either ingest fewer logs or shorten retention - due to the high cost and unreliability of storing log data at scale.
Performance and Time to Insights Suffers at Scale
An Elasticsearch cluster requires active management to spin up and spin down servers to accommodate high and query volumes. At scale, an Elasticsearch cluster can cause queries to take hours to complete or they time out, frustrating end users and causing blind spots in analysis.
Data Movement and Transformation Add Complexity
With Elasticsearch, you need to push data and transform in Logstash or Fluentd. You also have to continually monitor Elasticsearch exceptions. Plus, you need to build resilient pipelines, configure the stack to ingest and parse logs, ensure log data consistency, reindex outdated indices, and much more.