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- DataFrame.checkpoint(eager: bool = True) → pyspark.sql.dataframe.DataFrame [source] ¶ Returns a checkpointed version of this DataFrame. Checkpointing can be used to truncate the logical plan of this DataFrame, which is especially useful in iterative algorithms where the plan may grow exponentially.
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pyspark.sql.DataFrame.checkpoint. ¶. Returns a checkpointed version of this DataFrame. Checkpointing can be used to truncate the logical plan of this DataFrame, which is especially useful in iterative algorithms where the plan may grow exponentially.
Mar 15, 2018 · A guide to understanding the checkpointing and caching in Apache Spark. Covers strengths and weaknesses of either and the various use cases of when either is appropriate to use.
- Adrian Chang
Mar 27, 2024 · In Spark streaming application, checkpoint helps to develop fault-tolerant and resilient Spark applications. It maintains intermediate state on fault-tolerant compatible file systems like HDFS, ADLS and S3 storage systems to recover from failures.
Feb 1, 2016 · But it is up to you to tell Apache Spark where to write its checkpoint information. On the other hand, persisting is about caching data mostly in memory, as this part of the documentation clearly indicates. So, it depends on what directory you gave to Apache Spark.
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map , reduce , join and window .
Nov 5, 2023 · Checkpointing is more fault tolerant as if the spark job encounters an error, you can still access the checkpoint through the distributed file system.
The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the Dataset/DataFrame API in Scala, Java, Python or R to express streaming aggregations, event-time windows, stream-to-batch joins, etc.
