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Delta lake Check points storage concept

User16826994223
Honored Contributor III

In which format the Checkpoints are stored in storage and , how does it help in delta to increase performance.

1 ACCEPTED SOLUTION

Accepted Solutions

User16826994223
Honored Contributor III

Delta Lake writes checkpoints as an aggregate state of a Delta table every 10 commits. These checkpoints serve as the starting point to compute the latest state of the table. Without checkpoints, Delta Lake would have to read a large collection of JSON files (โ€œdeltaโ€ files) representing commits to the transaction log to compute the state of a table. In addition, the column-level statistics Delta Lake uses to perform data skipping are stored in the checkpoint.

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User16826994223
Honored Contributor III

Delta Lake writes checkpoints as an aggregate state of a Delta table every 10 commits. These checkpoints serve as the starting point to compute the latest state of the table. Without checkpoints, Delta Lake would have to read a large collection of JSON files (โ€œdeltaโ€ files) representing commits to the transaction log to compute the state of a table. In addition, the column-level statistics Delta Lake uses to perform data skipping are stored in the checkpoint.

User16826994223
Honored Contributor III

In Databricks Runtime 7.2 and below, column-level statistics are stored in Delta Lake checkpoints as a JSON column.

In Databricks Runtime 7.3 LTS and above, column-level statistics are stored as a struct. The struct format makes Delta Lake reads much faster, because:

  • Delta Lake doesnโ€™t perform expensive JSON parsing to obtain column-level statistics.
  • Parquet column pruning capabilities significantly reduce the I/O required to read the statistics for a column.

The struct format enables a collection of optimizations that reduce the overhead of Delta Lake read operations from seconds to tens of milliseconds, which significantly reduces the latency for short queries.

aladda
Honored Contributor II

Great points above on how checkpointing helps with performance. In additional Delta Lake also provides other data organization strategies such as compaction, Z-ordering to help with both read and write performance of Delta Tables. Additional details here - https://docs.databricks.com/delta/optimizations/file-mgmt.html

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