How Netflix Built a Distributed Write Ahead Log For Its Data Platform

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Disclaimer: The details in this post have been derived from the details shared online by the Netflix Engineering Team. All credit for the technical details goes to the Netflix Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.

Netflix processes an enormous amount of data every second. Each time a user plays a show, rates a movie, or receives a recommendation, multiple databases and microservices work together behind the scenes. This functionality is supported using hundreds of independent systems that must stay consistent with each other. When something goes wrong in one system, it can quickly create a ripple effect across the platform.

Netflix’s engineering team faced several recurring issues that threatened the reliability of their data. Some of these included accidental data corruption after schema changes, inconsistent updates between storage systems such as Apache Cassandra and Elasticsearch, and message delivery failures during transient outages. At times, bulk operations like large delete jobs even caused key-value database nodes to run out of memory. On top of that, some databases lacked built-in replication, which meant that regional failures could lead to permanent data loss.

Each engineering team tried to handle these issues differently. One team would build custom retry systems, another would design its own backup strategy, and yet another would use Kafka directly for message delivery. While these solutions worked individually, they created complexity and inconsistent guarantees across Netflix’s ecosystem. Over time, this patchwork approach increased maintenance costs and made debugging