Garbage collector documentation and configuration

Incminimark

PyPy’s default garbage collector is called incminimark - it’s an incremental, generational moving collector. Here we hope to explain a bit how it works and how it can be tuned to suit the workload.

Incminimark first allocates objects in so called nursery - place for young objects, where allocation is very cheap, being just a pointer bump. The nursery size is a very crucial variable - depending on your workload (one or many processes) and cache sizes you might want to experiment with it via PYPY_GC_NURSERY environment variable. When the nursery is full, there is performed a minor collection. Freed objects are no longer referencable and just die, just by not being referenced any more; on the other hand, objects found to still be alive must survive and are copied from the nursery to the old generation. Either to arenas, which are collections of objects of the same size, or directly allocated with malloc if they’re larger. (A third category, the very large objects, are initially allocated outside the nursery and never move.)

Since Incminimark is an incremental GC, the major collection is incremental, meaning there should not be any pauses longer than 1ms.

Fragmentation

Before we discuss issues of “fragmentation”, we need a bit of precision. There are two kinds of related but distinct issues:

  • If the program allocates a lot of memory, and then frees it all by dropping all references to it, then we might expect to see the RSS to drop. (RSS = Resident Set Size on Linux, as seen by “top”; it is an approximation of the actual memory usage from the OS’s point of view.) This might not occur: the RSS may remain at its highest value. This issue is more precisely caused by the process not returning “free” memory to the OS. We call this case “unreturned memory”.
  • After doing the above, if the RSS didn’t go down, then at least future allocations should not cause the RSS to grow more. That is, the process should reuse unreturned memory as long as it has got some left. If this does not occur, the RSS grows even larger and we have real fragmentation issues.

gc.get_stats

There is a special function in the gc module called get_stats(memory_pressure=False).

memory_pressure controls whether or not to report memory pressure from objects allocated outside of the GC, which requires walking the entire heap, so it’s disabled by default due to its cost. Enable it when debugging mysterious memory disappearance.

Example call looks like that:

>>> gc.get_stats(True)
Total memory consumed:
GC used:            4.2MB (peak: 4.2MB)
   in arenas:            763.7kB
   rawmalloced:          383.1kB
   nursery:              3.1MB
raw assembler used: 0.0kB
memory pressure:    0.0kB
-----------------------------
Total:              4.2MB

Total memory allocated:
GC allocated:            4.5MB (peak: 4.5MB)
   in arenas:            763.7kB
   rawmalloced:          383.1kB
   nursery:              3.1MB
raw assembler allocated: 0.0kB
memory pressure:    0.0kB
-----------------------------
Total:                   4.5MB

In this particular case, which is just at startup, GC consumes relatively little memory and there is even less unused, but allocated memory. In case there is a lot of unreturned memory or actual fragmentation, the “allocated” can be much higher than “used”. Generally speaking, “peak” will more closely resemble the actual memory consumed as reported by RSS. Indeed, returning memory to the OS is a hard and not solved problem. In PyPy, it occurs only if an arena is entirely free—a contiguous block of 64 pages of 4 or 8 KB each. It is also rare for the “rawmalloced” category, at least for common system implementations of malloc().

The details of various fields:

  • GC in arenas - small old objects held in arenas. If the amount “allocated” is much higher than the amount “used”, we have unreturned memory. It is possible but unlikely that we have internal fragmentation here. However, this unreturned memory cannot be reused for any malloc(), including the memory from the “rawmalloced” section.
  • GC rawmalloced - large objects allocated with malloc. This is gives the current (first block of text) and peak (second block of text) memory allocated with malloc(). The amount of unreturned memory or fragmentation caused by malloc() cannot easily be reported. Usually you can guess there is some if the RSS is much larger than the total memory reported for “GC allocated”, but do keep in mind that this total does not include malloc’ed memory not known to PyPy’s GC at all. If you guess there is some, consider using jemalloc as opposed to system malloc.
  • nursery - amount of memory allocated for nursery, fixed at startup, controlled via an environment variable
  • raw assembler allocated - amount of assembler memory that JIT feels responsible for
  • memory pressure, if asked for - amount of memory we think got allocated via external malloc (eg loading cert store in SSL contexts) that is kept alive by GC objects, but not accounted in the GC

Environment variables

PyPy’s default incminimark garbage collector is configurable through several environment variables:

PYPY_GC_NURSERY
The nursery size. Defaults to 1/2 of your last-level cache, or 4M if unknown. Small values (like 1 or 1KB) are useful for debugging.
PYPY_GC_NURSERY_DEBUG
If set to non-zero, will fill nursery with garbage, to help debugging.
PYPY_GC_INCREMENT_STEP
The size of memory marked during the marking step. Default is size of nursery times 2. If you mark it too high your GC is not incremental at all. The minimum is set to size that survives minor collection times 1.5 so we reclaim anything all the time.
PYPY_GC_MAJOR_COLLECT
Major collection memory factor. Default is 1.82, which means trigger a major collection when the memory consumed equals 1.82 times the memory really used at the end of the previous major collection.
PYPY_GC_GROWTH
Major collection threshold’s max growth rate. Default is 1.4. Useful to collect more often than normally on sudden memory growth, e.g. when there is a temporary peak in memory usage.
PYPY_GC_MAX
The max heap size. If coming near this limit, it will first collect more often, then raise an RPython MemoryError, and if that is not enough, crash the program with a fatal error. Try values like 1.6GB.
PYPY_GC_MAX_DELTA
The major collection threshold will never be set to more than PYPY_GC_MAX_DELTA the amount really used after a collection. Defaults to 1/8th of the total RAM size (which is constrained to be at most 2/3/4GB on 32-bit systems). Try values like 200MB.
PYPY_GC_MIN
Don’t collect while the memory size is below this limit. Useful to avoid spending all the time in the GC in very small programs. Defaults to 8 times the nursery.
PYPY_GC_DEBUG
Enable extra checks around collections that are too slow for normal use. Values are 0 (off), 1 (on major collections) or 2 (also on minor collections).
PYPY_GC_MAX_PINNED
The maximal number of pinned objects at any point in time. Defaults to a conservative value depending on nursery size and maximum object size inside the nursery. Useful for debugging by setting it to 0.