sdf_persist: Persist a Spark DataFrame
In rstudio/sparklyr: R Interface to Apache Spark
View source: R/sdf_interface.R
sdf_persist R Documentation
Persist a Spark DataFrame
Description
Persist a Spark DataFrame, forcing any pending computations and (optionally)
serializing the results to disk.
Usage
sdf_persist(x, storage.level = "MEMORY_AND_DISK", name = NULL)
Arguments
x
A spark_connection, ml_pipeline, or a tbl_spark.
storage.level
The storage level to be used. Please view the
Spark Documentation
for information on what storage levels are accepted.
name
A name to assign this table. Passed to [sdf_register()].
Details
Spark DataFrames invoke their operations lazily – pending operations are
deferred until their results are actually needed. Persisting a Spark
DataFrame effectively 'forces' any pending computations, and then persists
the generated Spark DataFrame as requested (to memory, to disk, or
otherwise).
Users of Spark should be careful to persist the results of any computations
which are non-deterministic – otherwise, one might see that the values
within a column seem to 'change' as new operations are performed on that
data set.
rstudio/sparklyr documentation built on Sept. 18, 2024, 6:10 a.m.
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View source: R/sdf_interface.R
| sdf_persist | R Documentation |
Persist a Spark DataFrame
Description
Persist a Spark DataFrame, forcing any pending computations and (optionally) serializing the results to disk.
Usage
sdf_persist(x, storage.level = "MEMORY_AND_DISK", name = NULL)
Arguments
x |
A |
storage.level |
The storage level to be used. Please view the Spark Documentation for information on what storage levels are accepted. |
name |
A name to assign this table. Passed to [sdf_register()]. |
Details
Spark DataFrames invoke their operations lazily – pending operations are deferred until their results are actually needed. Persisting a Spark DataFrame effectively 'forces' any pending computations, and then persists the generated Spark DataFrame as requested (to memory, to disk, or otherwise).
Users of Spark should be careful to persist the results of any computations which are non-deterministic – otherwise, one might see that the values within a column seem to 'change' as new operations are performed on that data set.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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