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Delta Lake time travel lets you query an earlier table snapshot by version or timestamp, so you can investigate a bad pipeline result or reproduce the dataset behind an ML run without changing the table’s current contents. It is useful only while the transaction-log history and the data files needed for that snapshot remain available; time travel is not an indefinite backup.
What Delta Lake time travel does
Delta Lake records table changes in a transaction log and uses that history with the retained data files to present a table snapshot from an earlier point. A query against that snapshot does not roll back the current table. This makes historical reads useful for comparing pipeline outputs, debugging or auditing a change, running temporal queries, and reconstructing data used in an analysis or model.
The Delta Lake project describes time travel as enabling “rollbacks, full historical audit trails, and reproducible machine learning experiments.” In practice, those outcomes depend on how long the relevant history and files are retained; a time-travel query alone does not create a permanent audit archive. Delta Lake: Query an older snapshot of a table
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How to read an earlier snapshot
Choose an explicit table version when reproducibility matters. Timestamp-based selection can be convenient for investigation, but timestamps may be affected by file and log timestamps—for example, if a table directory has been copied. Record the version identifier with the run rather than relying only on a date or time.
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Use the Delta reader options versionAsOf or timestampAsOf when loading a table. The following shows the version-based pattern; substitute the path and version for your environment:
spark.read.format("delta").option("versionAsOf", 42).load("/path/to/table")SQL
Delta SQL supports version-based and timestamp-based reads with VERSION AS OF and timestamp-based time-travel syntax. Confirm the syntax and API availability for the Spark and Delta Lake versions deployed in your environment. The Delta Lake quick start currently describes setup compatible with Delta Lake 4.0.0 and demonstrates version-based reads; it is not a guarantee that every existing Spark installation supports that version.
Use a table version to make an ML run reproducible
Store the selected Delta table version in the experiment or pipeline metadata alongside the model code and other run inputs. When you need to reproduce a training run, read that recorded version with versionAsOf. This ties the experiment to a precise table snapshot rather than to whatever data happens to be current when the run is repeated. The Delta Lake mechanics support version-addressed reads; recording the version as run metadata is the practical step that makes those reads useful for lineage.
If you need to preserve a training dataset separately for testing or later use, a shallow clone can provide a copy of a selected table state. A clone has its own history: its version numbers do not map directly to the source table’s versions. Record the clone’s identity as well as the source snapshot from which it was created. See Delta Lake shallow and deep clones.
Read an old version or restore it as current?
| Choice | What it does | When it fits | Important consequence |
|---|---|---|---|
| Historical read | Queries an earlier snapshot without changing the current table. | Investigating a pipeline result, comparing data, or reproducing an ML input. | Requires the relevant log history and data files to remain available. |
RESTORE |
Commits a new table version whose contents correspond to an earlier state. | Returning the table’s current contents to a prior state. | Downstream streaming consumers may treat the restore’s change records as new data and process records again. |
Before using RESTORE, check how downstream jobs interpret its data-change records and whether they can tolerate duplicate processing. Validate the result and downstream behavior rather than treating restore as equivalent to a read-only historical query. Delta Lake: Restore a Delta table to an earlier state
Retention sets the recovery window
Two separate retention settings matter. The current Delta Lake documentation lists a default of interval 30 days for delta.logRetentionDuration, which governs retained log history, and interval 7 days for delta.deletedFileRetentionDuration, the minimum age before logically deleted files become eligible for VACUUM. These are documented defaults, not universal guarantees: table properties and deployment behavior can differ. Delta Lake: Data retention
VACUUM is not automatic; it removes eligible files that are no longer referenced by the table. If it removes files needed to reconstruct an old snapshot, time travel to versions relying on those files is no longer possible. Log cleanup is separate and can also limit which historical versions can be reconstructed. Consequently, a 30-day log-history setting does not by itself ensure 30 days of readable data if required files have already been vacuumed.
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Time travel is not a perpetual change log
Time travel reconstructs snapshots only within the limits of retained history and files. It should not be treated as a permanent audit trail. Delta Change Data Feed is a separate feature: it must be explicitly enabled, captures changes only after it is enabled, and its change records are subject to table retention and can be removed by VACUUM. If your requirement is a durable record of every change, define and operate an archival strategy rather than assuming time travel or Change Data Feed preserves records indefinitely. Delta Lake: Change Data Feed
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