RDD存储位置与Spark持久化存储相关技术问题咨询

Let’s break down your questions one by one to make everything crystal clear:

1. Where are RDDs stored? #

RDDs are distributed by design—their data is split into partitions, and each partition lives on the Executor nodes (in your setup, these Executors run on your Hadoop DataNodes). The Driver node (which might be hosted on your NameNode, though note NameNode’s core role is HDFS metadata management) only keeps track of RDD metadata (like partition locations, lineage) — it never stores the actual RDD data itself.

2. Where does dataframe.persist(MEMORY_AND_DISK) store data? #

When you use this persistence strategy:

• Spark first tries to store the DataFrame’s partitions in the heap memory of the Executors (residing on your DataNodes).
• If Executor heap memory runs out, the excess partitions get written to the local disk of the Executor’s host node (so the DataNode’s local filesystem, not HDFS).

Key points to note:

• Neither the NameNode nor the Spark Driver stores any cached data—they only manage metadata about what’s cached and where it lives.
• The NameNode has no direct role in Spark’s cache storage; it’s only responsible for HDFS file system metadata.

3. Does cached data depend on heap memory size? #

Absolutely. The MEMORY_AND_DISK strategy prioritizes in-memory storage first. The size of the Executor’s heap memory directly determines how much data can stay in memory (for fast access) before spilling to disk (which is slower). A smaller heap will lead to more disk I/O for cached data, which can slow down your job performance.

4. How to increase heap memory for all nodes? #

You can adjust memory settings either per application or globally:

Per-application (via spark-submit) #

When launching your Spark job, use these flags to set memory on the fly:

spark-submit \
  --driver-memory 8g \  # Adjust Driver heap size if needed
  --executor-memory 16g \  # Set heap size for each Executor
  --num-executors 3 \  # Match your 3 DataNodes if desired
  your-spark-app.jar

Global configuration (persistent for all jobs) #

Edit the spark-defaults.conf file in your Spark installation directory to set default memory values:

spark.driver.memory 8g
spark.executor.memory 16g

For YARN clusters (since you’re using Hadoop)

You also need to ensure YARN allows these memory allocations:

• Edit yarn-site.xml on your NameNode and all DataNodes:

  <property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>20480</value> <!-- Total memory available per node (e.g., 20GB) -->
  </property>
  <property>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>16384</value> <!-- Max memory per container (matches executor-memory) -->
  </property>
  

• Restart YARN services after making these changes for them to take effect.

内容的提问来源于stack exchange，提问作者Arun