The Log: What every software engineer should know about real-time data's unifying abstraction

Here is the link.

One of the most useful things I learned in all this was that many of the things we were building had a very simple concept at their heart: the log. Sometimes called write-ahead logs or commit logs or transaction logs, logs have been around almost as long as computers and are at the heart of many distributed data systems and real-time application architectures.

To make this atomic and durable, a database uses a log to write out information about the records they will be modifying, before applying the changes to all the various data structures it maintains. The log is the record of what happened, and each table or index is a projection of this history into some useful data structure or index. Since the log is immediately persisted it is used as the authoritative source in restoring all other persistent structures in the event of a crash.

For a long time, Kafka was a little unique (some would say odd) as an infrastructure product—neither a database nor a log file collection system nor a traditional messaging system. But recently Amazon has offered a service that is very very similar to Kafka called Kinesis. The similarity goes right down to the way partitioning is handled, data is retained, and the fairly odd split in the Kafka API between high- and low-level consumers. I was pretty happy about this. A sign you've created a good infrastructure abstraction is that AWS offers it as a service! Their vision for this seems to be exactly similar to what I am describing: it is the piping that connects all their distributed systems—DynamoDB, RedShift, S3, etc.—as well as the basis for distributed stream processing using EC2.

Relationship to ETL and the Data Warehouse

The key problem for a data-centric organization is coupling the clean integrated data to the data warehouse. A data warehouse is a piece of batch query infrastructure which is well suited to many kinds of reporting and ad hoc analysis, particularly when the queries involve simple counting, aggregation, and filtering. But having a batch system be the only repository of clean complete data means the data is unavailable for systems requiring a real-time feed—real-time processing, search indexing, monitoring systems, etc.