The Internals of Delta Lake 0.5.0

Delta Lake is an open-source storage management system (storage layer) that brings ACID transactions and time travel to Apache Spark and big data workloads.

Delta Lake introduces a concept of delta table that is simply a parquet table with a transactional log.

Changes to (the state of) a delta table are reflected as actions and persisted to the transactional log (in JSON format).

Delta Lake uses OptimisticTransaction for transactional writes. A commit is successful when the transaction can write the actions to a delta file (in the transactional log). In case the delta file for the commit version already exists, the transaction is retried.

Structured queries can write (transactionally) to a delta table using the following interfaces:

  • WriteIntoDelta command for batch queries (Spark SQL)

  • DeltaSink for streaming queries (Spark Structured Streaming)

More importantly, multiple queries can write to the same delta table simultaneously (at exactly the same time).

Delta Lake provides DeltaTable API to programmatically access Delta tables. A delta table can be created based on a parquet table (DeltaTable.convertToDelta) or from scratch (DeltaTable.forPath).

Delta Lake supports Spark SQL and Structured Streaming using delta format.

Delta Lake supports reading and writing in batch queries:

Delta Lake supports reading and writing in streaming queries:

Delta Lake uses LogStore abstraction to read and write physical log files and checkpoints (using Hadoop FileSystem API).

Installing Delta Lake

In order to "install" and use Delta Lake in a Spark application (e.g. spark-shell), use --packages command-line option.

./bin/spark-shell \
  --packages \
assert(spark.version.matches("2.4.[2-4]"), "Delta Lake supports Spark 2.4.2+")

val input = spark
  .option("path", "delta")

delta data source requires some options (with path option required).