On-heap memory is allocated in the JVM’s Java heap and reclaimed by garbage collection; off-heap memory sits outside that heap and needs a separately managed lifetime. Off-heap can reduce pressure from Java objects, but it does not shrink the heap or the process’s total memory budget. In Spark, that distinction matters when sizing executors and investigating why a process uses more memory than its -Xmx limit.
What is the difference between on-heap and off-heap memory?
| Aspect | On-heap | Off-heap |
|---|---|---|
| Where it lives | In the Java heap, where Java objects are allocated. | Outside the Java heap, in memory managed separately from ordinary Java objects. |
| Reclamation | The garbage collector reclaims space occupied by objects that are no longer reachable. | Normal heap garbage collection does not reclaim it simply because the application no longer needs it; the application or owning API must release it. |
| Ownership and leak risk | Reachability determines whether an object can be collected, though references that are retained accidentally can still keep objects alive. | Code must define ownership and release timing. A missed release can retain native or direct memory even when the related work is finished. |
| Typical fit | General Java objects when simple ownership and automatic reclamation are useful. | Large buffers, native/JNI interoperability, or cases where object count and heap scanning are bottlenecks. |
Oracle defines on-heap memory as memory in the Java heap, a region managed by the garbage collector, and off-heap memory as memory outside that heap: Oracle Java garbage-collector implementation. Some Java APIs provide structured lifetimes—for example, MemorySegment arenas—rather than leaving deallocation entirely to ad hoc application code.
What garbage collection does—and does not—reclaim
Garbage collection identifies heap objects that are no longer reachable and reuses their heap space. It does not make every allocation disappear immediately: reachable objects remain, and collection timing depends on the JVM and workload. Nor does ordinary heap collection reclaim memory merely because it was allocated outside the Java heap. Off-heap allocations need their own release path and monitoring.
This distinction helps explain why a process can retain substantial memory after a heap collection. Heap usage may fall while direct buffers, native libraries, thread stacks, JIT/compiler data, or other native allocations remain. A heap dump can help investigate Java object retention, but it is not a complete inventory of all process memory.
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Not automatically. In Spark, spark.memory.offHeap.size reserves a separate off-heap memory budget; Spark explicitly says this setting has no impact on heap memory usage. If an executor must fit a hard container limit, lowering the off-heap allocation alone is not a substitute for sizing the heap and other memory components together. See the Spark configuration reference.
Off-heap may let a workload store selected data outside the heap, which can reduce the number or size of heap objects involved. That is an application design change, not a side effect of enabling off-heap. The heap still needs room for the Java objects and runtime work the application continues to perform.
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Why can Java objects use more memory than their fields suggest?
Raw field data is only part of an object’s footprint. Object headers, references, alignment, and the overhead of many small objects add up. Apache Spark documentation notes that Java objects can use 2–5 times the space of the raw data in their fields; this is a documented rule of thumb, not a guarantee for every JVM, object layout, or workload. Spark recommends measuring actual object use rather than assuming that field sizes equal heap consumption.
Before moving data off-heap, consider whether the same heap data can be represented more compactly: use primitive-oriented layouts where appropriate, avoid unnecessary wrapper objects and object proliferation, or store data in serialized form. Serialized storage can reduce footprint, but accessing it may require deserialization and add CPU cost. Measure the trade-off for the workload instead of assuming either representation is faster.
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Spark’s unified memory model shares a region between execution and storage. Execution memory supports tasks such as shuffles, joins, and sorts; storage memory is used for cached or persisted data. Execution can reclaim storage memory when needed, but only down to the protected storage region, R. The protection is controlled by spark.memory.storageFraction; it is a boundary within the unified region, not a separate pool added on top.
The documented defaults in Spark’s current configuration reference are:
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| Setting | Default | What it controls |
|---|---|---|
spark.memory.fraction |
0.6 | Fraction of heap memory, after subtracting 300 MB, available to Spark’s unified execution-and-storage region. |
spark.memory.storageFraction |
0.5 | Protected storage portion of that unified region; execution may evict storage only down to this boundary. |
spark.memory.offHeap.enabled |
false | Whether Spark’s off-heap memory management is enabled. |
spark.memory.offHeap.size |
Not stated as a default size in the configuration reference. | Off-heap memory budget; it must be positive when off-heap is enabled and does not reduce heap usage. |
These values describe Spark’s configuration model, not a promise that a particular application will have enough memory. The Spark tuning guide explains the unified model and recommends measuring object usage and garbage-collection behavior.
How much memory overhead does Spark need?
There is no single executor-overhead figure that fits every Spark job. Container or executor memory accounting includes more than the JVM heap: Spark’s executor limit combines executor heap, memory overhead, configured off-heap size, and optional PySpark memory. The exact applicable limit and defaults depend on the Spark version, cluster manager, and configuration, so consult the configuration reference for the deployment rather than treating -Xmx as the full process budget.
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Memory overhead covers non-heap use such as native allocations and other process memory. Python workers, direct buffers, native libraries, thread stacks, and workload-specific behavior can all affect what fits. If a container is killed while heap usage appears below -Xmx, compare the container’s total memory against the combined budget and investigate non-heap consumers; increasing heap without increasing the container limit can make the mismatch worse.
How to measure heap and off-heap use
Measure Spark object and storage use
Use the Spark UI’s Storage view to inspect persisted data, and use Spark’s SizeEstimator to estimate the memory footprint of objects. These are estimates and views of Spark-managed data, not a complete accounting of every native allocation in the executor process.
Measure garbage-collection pressure
Enable and inspect JVM GC logs to see how often collections occur and how much time they consume. Frequent or long collections can indicate heap pressure, but logs should be considered alongside workload behavior and actual heap occupancy.
Investigate process memory beyond the heap
Compare JVM heap metrics with process or container memory measurements. A gap can point to off-heap or other native use, but it does not identify the source on its own. Where supported by the JVM and deployment, native-memory tracking can help categorize native allocations; review the relevant JVM documentation and operational tooling for the exact version in use.
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Prefer on-heap when
- Ordinary Java object ownership and garbage-collected reclamation make the code simpler.
- GC frequency and pause time are acceptable for the workload.
- The data structures are not creating excessive object overhead or heap pressure.
Consider off-heap when
- Large buffers or native/JNI interoperability are central to the workload.
- Heap object count, object scanning, or garbage-collection pressure is a measured bottleneck.
- The application can enforce clear ownership, release lifetimes, and monitoring for allocations outside the heap.
There is no universal rule that off-heap access is faster. Performance depends on allocation strategy, access patterns, serialization, GC behavior, and application architecture. Treat off-heap as an additional memory pool with its own costs and failure modes, not as a replacement for a correctly sized heap.
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