This is an automated email from the ASF dual-hosted git repository.
uros-b pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/master by this push:
new f9251252cfd9 [SPARK-58639][DOC] Replace stale listCachedTables
reference in the SQL performance tuning guide
f9251252cfd9 is described below
commit f9251252cfd9282dcd154eb5eec7c9981c169e8d
Author: Uros Bojanic <[email protected]>
AuthorDate: Mon Aug 10 04:31:02 2026 +0000
[SPARK-58639][DOC] Replace stale listCachedTables reference in the SQL
performance tuning guide
### What changes were proposed in this pull request?
Replaces a stale sentence in the caching section of
`docs/sql-performance-tuning.md`. The old text told users to call
`spark.catalog.listCachedTables()`; the new text points to
`spark.catalog.isCached`, the `Dataset.storageLevel` property, and the web UI
Storage tab.
### Why are the changes needed?
`spark.catalog.listCachedTables()` no longer exists. It was removed by the
SPARK-56221 followup (`a3930d31665`) along with the `SHOW CACHED TABLES`
command. That commit edited this sentence to drop the SQL command but left the
`listCachedTables()` call in place, so the guide currently points at a method
that cannot be called. This is the only remaining reference to the removed API
in the repository.
Each replacement is a documented API: `Catalog.isCached`
(`sql/api/.../catalog/Catalog.scala`), `Dataset.storageLevel`, whose
implementation returns `StorageLevel.NONE` when the data is not cached, and the
Storage tab, which `docs/web-ui.md` describes as showing "the storage levels,
sizes and partitions of all RDDs" and notes does not list a relation "before
they are materialized". The Storage tab also covers data cached via
`Dataset.cache()` without a name, which is what the removed [...]
### Does this PR introduce _any_ user-facing change?
No, other than the corrected documentation.
### How was this patch tested?
Documentation only. The `#storage-tab` anchor matches the `## Storage Tab`
heading in `docs/web-ui.md`, and the link style matches the existing `[SQL
UI](web-ui.html#sql-tab)` reference in this same file.
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Opus 4.8)
Closes #57846 from uros-b/doc-cached-tables-api.
Authored-by: Uros Bojanic <[email protected]>
Signed-off-by: Uros Bojanic <[email protected]>
---
docs/sql-performance-tuning.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index 3822c56bc3b9..36dfb2e47fc3 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -30,7 +30,7 @@ Spark SQL can cache tables using an in-memory columnar format
by calling `spark.
Then Spark SQL will scan only required columns and will automatically tune
compression to minimize
memory usage and GC pressure. You can call
`spark.catalog.uncacheTable("tableName")` or `dataFrame.unpersist()` to remove
the table from memory.
-To list relations cached with an explicit name, use
`spark.catalog.listCachedTables()`. Entries cached only via `Dataset.cache()`
without a name are not included.
+To check whether a specific table or view is cached, use
`spark.catalog.isCached("tableName")`. To inspect the storage level of an
arbitrary `Dataset`, read its `storageLevel` property, which returns
`StorageLevel.NONE` when the data is not currently cached. For an overview of
everything persisted in the running application, including data cached directly
via `Dataset.cache()`, use the [Storage tab](web-ui.html#storage-tab) of the
web UI, which shows the storage levels, sizes and partiti [...]
Spark supports two cache formats:
- **Default cache format**: The standard in-memory columnar cache (used by
default).
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
