How Does Apache Spark Manage Executor Memory?

TL;DR

Apache Spark manages executor memory by dividing it into JVM-managed on-heap memory, overhead memory, and optional off-heap memory, while dynamically sharing unified memory between execution and storage. In the 10 GB example, unified memory gets 6 GB, initially split into 3 GB each for execution and storage. Read on to see how Spark reallocates space, evicts cached blocks, and calculates its container request.

Transcript

do you know that understanding memory management can help you solve a wide variety of optimization problems in spark so memory management has been a crucial topic a tricky topic for most of us to understand but at the same time it is very important in order to develop a solid grasp over two things the first one is how spark Works in and the second ... Read More

Key Insights

• Spark executor memory is organized into on-heap memory, overhead memory, and optional off-heap memory. Most Spark operations run in the on-heap region, which is managed by the Java Virtual Machine because Spark's underlying execution occurs on the JVM.
• Execution memory is the on-heap area used for joins, shuffles, sorting, and aggregations. These essential computation activities give execution memory priority when it competes with storage memory for space inside Spark's unified memory region.
• Storage memory is the on-heap area used for caching RDDs or DataFrames and storing broadcast variables. It shares unified memory with execution, so its effective capacity can change dynamically according to the executor's computational and caching requirements.
• User memory is used for program-defined objects such as variables, lists, sets, dictionaries, and user-defined functions. Reserved memory is a separate 300 MB portion that Spark requires to run itself and store internal objects.
• Unified memory is the combined execution and storage region controlled through Spark's dynamic memory management strategy. Its boundary is movable, allowing either side to use vacant capacity that was initially associated with the other side.
• Execution memory has priority over storage memory because joins, shuffles, sorts, and group operations depend on it. When execution requires occupied storage space, Spark can evict storage blocks using the least recently used algorithm to create room.
• A 10 GB executor with a 0.6 memory fraction assigns 6 GB to unified memory. Using the stated default storage fraction of 0.5 initially gives storage 3 GB and execution 3 GB, while the remaining 4 GB covers user and reserved memory.
• Container memory is calculated beyond the configured executor memory because that setting covers only on-heap memory. In the example, 10 GB of executor memory plus 1 GB of overhead produces an 11 GB cluster-manager request when off-heap memory is disabled.

Questions & Answers

Q: How does Apache Spark manage executor memory?

Spark divides executor memory into on-heap memory, overhead memory, and optional off-heap memory. The on-heap area contains execution, storage, user, and reserved memory, while execution and storage dynamically share a unified region. Overhead and enabled off-heap memory are separate from the configured on-heap executor memory.

Q: What are the main parts of Spark's on-heap memory?

Spark divides on-heap memory into execution, storage, user, and reserved memory. Execution handles joins, shuffles, sorts, and aggregations, while storage holds cached RDDs, cached DataFrames, and broadcast variables. User memory stores program objects and user-defined functions, and Spark reserves 300 MB for its own operation and internal objects.

Q: What is Spark execution memory used for?

Execution memory supports joins, shuffles, sorting, aggregations, and grouping operations. It shares unified memory with storage and receives priority when both regions need the same capacity. It can use vacant storage space or obtain more room by evicting cached storage blocks.

Q: What is Spark storage memory used for?

Storage memory holds cached RDDs, cached DataFrames, and broadcast variables. Its boundary with execution memory is movable, so it may use execution space that is currently available. However, Spark can evict storage blocks when execution needs occupied capacity.

Q: How is 10 GB of Spark executor memory divided in the example?

With a memory fraction of 0.6, 6 GB of the 10 GB executor becomes unified memory. A storage fraction of 0.5 initially assigns 3 GB to storage and 3 GB to execution. The remaining 4 GB includes 300 MB of reserved memory, leaving approximately 3.7 GB for user memory under the stated conversion.

Q: Why are execution and storage called unified memory in Spark?

They are called unified memory because Spark manages them as a shared region with a movable boundary. Execution can consume unused storage capacity, and storage can use available execution capacity. This arrangement lets their effective sizes change according to computational and caching needs.

Q: What happens when Spark execution memory needs more space?

Execution first uses any vacant capacity in the storage portion of unified memory. If storage already occupies that space, Spark can evict cached storage blocks using the least recently used algorithm. Execution has priority because operations such as joins, shuffles, sorts, and aggregations depend on it.

Q: How do overhead and off-heap memory affect Spark's container request?

Configured executor memory covers only the on-heap region, so overhead and enabled off-heap memory are added to the cluster manager's container request. In the example, overhead is the greater of 384 MB or 10 percent of executor memory, making it 1 GB for a 10 GB executor. With off-heap memory disabled, the resulting request is 11 GB.

Summary & Key Takeaways

• Spark executor memory includes a large JVM-managed on-heap area, a separate overhead area, and optional off-heap memory. The on-heap area contains execution memory for joins, shuffles, sorts, and aggregations, storage memory for cached data and broadcast variables, user memory for program objects, and 300 MB of reserved memory.

• For a 10 GB executor using the stated 0.6 memory fraction, unified memory receives 6 GB. With a 0.5 storage fraction, storage initially receives 3 GB and execution receives 3 GB. The remaining 4 GB includes 300 MB of reserved memory, leaving approximately 3.7 GB for user memory.

• Unified memory allows execution and storage to share available capacity through a movable boundary. Execution can consume unused storage space and may force storage blocks to be evicted using the least recently used algorithm. Executor memory covers only on-heap memory, so overhead and enabled off-heap memory increase the cluster manager's container request.