- asynchronous cache loader support
- optional per entry or default TTL/expireAt
- entry eviction and expiration without a separate thread
- capable of maintaining huge amounts of cache memory
- suitable for tiny/small entries with low overhead using the chunked implementation
- runs with Java 8 and Java 11 - support for Java 7 and earlier has been dropped with version 0.7.0
- to build OHC from source, Java 11 or newer (tested with Java 11 + 15) is required
OHC shall provide a good performance on both commodity hardware and big systems using non-uniform-memory-architectures.
No performance test results available yet - you may try the ohc-benchmark tool. See instructions below. A very basic impression on the speed is in the _Benchmarking_ section.
Java 8 VM that support 64bit and has sun.misc.Unsafe (Oracle JVMs on x64 Intel CPUs).
OHC is targeted for Linux and OSX. It should work on Windows and other Unix OSs.
OHC provides two implementations for different cache entry characteristics: - The _linked_ implementation allocates off-heap memory for each entry individually and works best for medium and big entries. - The _chunked_ implementation allocates off-heap memory for each hash segment as a whole and is intended for small entries.
The number of segments is configured via org.caffinitas.ohc.OHCacheBuilder, defaults to # of cpus * 2 and must
be a power of 2. Entries are distribtued over the segments using the most significant bits of the 64 bit hash code.
Accesses on each segment are synchronized.
Each hash-map entry is allocated individually. Entries are free'd (deallocated), when they are no longer referenced by
the off-heap map itself or any external reference like org.caffinitas.ohc.DirectValueAccess or a
org.caffinitas.ohc.CacheSerializer.
The design of this implementation reduces the locked time of a segment to a very short time. Put/replace operations
allocate memory first, call the org.caffinitas.ohc.CacheSerializer to serialize the key and value and then put the
fully prepared entry into the segment.
Eviction is performed using an LRU algorithm. A linked list through all cached elements per segment is used to keep track of the eldest entries.
Chunked memory allocation off-heap implementation.
Purpose of this implementation is to reduce the overhead for relatively small cache entries compared to the linked
implementation since the memory for the whole segment is pre-allocated. This implementation is suitable for small
entries with fast (de)serialization implementations of org.caffinitas.ohc.CacheSerializer.
Segmentation is the same as in the linked implementation. The number of segments is configured via
org.caffinitas.ohc.OHCacheBuilder, defaults to # of cpus * 2 and must be a power of 2. Entries are distribtued
over the segments using the most significant bits of the 64 bit hash code. Accesses on each segment are synchronized.
Each segment is divided into multiple chunks. Each segment is responsible for a portion of the total capacity
(capacity / segmentCount). This amount of memory is allocated once up-front during initialization and logically
divided into a configurable number of chunks. The size of each chunk is configured using the chunkSize option in
org.caffinitas.ohc.OHCacheBuilder.
Like the linked implementation, hash entries are serialized into a temporary buffer first, before the actual put into a segment occurs (segement operations are synchronized).
New entries are placed into the current write chunk. When that chunk is full, the next empty chunk will become the new write chunk. When all chunks are full, the least recently used chunk, including all the entries it contains, is evicted.
Specifying the fixedKeyLength and fixedValueLength builder properties reduces the memory footprint by
8 bytes per entry.
Serialization, direct access and get-with-loader functions are not supported in this implementation.
To enable the chunked implementation, specify the chunkSize in org.caffinitas.ohc.OHCacheBuilder.
Note: the chunked implementation should still be considered experimental.
OHC supports three eviction algorithms:
- LRU: The oldest (least recently used) entries are evicted to make room for new entries.
- Window Tiny-LFU: Entries with lower usage frequency are evicted to make room for new entries. The goal of this eviction algorithm is to prevent heavily used entries from being evicted. Note that the maximum size of entries is limited to the size of the eden generation, which is currently fixed at 20% of the segment size (i.e. overall capacity / number of segments). Each OHC cache segment is divided into an eden and a main "generation". New entries start in the eden generation to give these time to build up their usage frequencies. When the eden generation becomes full, entries in the eden generation have to pass the admission filter, which checks the frequencies of the entries in the eden generation against the frequencies of the oldest (least recently used) entries in the main generation. See this article for a more thorough description. (Only supported in the _linked_ implementation, not supported by the chunked implementation)
- None: OHC performs no eviction on its own. It is up to the caller to check the return values and monitor free capacity. (Only supported in the _linked_ implementation, not supported by the chunked implementation)
Use the class OHCacheBuilder to configure all necessary parameter like
- number of segments (must be a power of 2), defaults to number-of-cores * 2
- hash table size (must be a power of 2), defaults to 8192
- load factor, defaults to .75
- capacity for data over the whole cache
- key and value serializers
- default TTL
- optional unlocked mode
Generally you should work with a large hash table. The larger the hash table, the shorter the linked-list in each hash partition - that means less linked-link walks and increased performance.
The total amount of required off heap memory is the total capacity plus hash table. Each hash bucket (currently)
requires 8 bytes - so the formula is capacity + segment_count * hash_table_size * 8.
OHC allocates off-heap memory directly bypassing Java's off-heap memory limitation. This means, that all
memory allocated by OHC is not counted towards -XX:maxDirectMemorySize.
Since especially the linked implementation performs alloc/free operations for each individual entry, consider that memory fragmentation can happen.
Also leave some head room since some allocations might still be in flight and also "the other stuff" (operating system, JVM, etc) need memory. It depends on the usage pattern how much head room is necessary. Note that the linked implementation allocates memory during write operations _before_ it is counted towards the segments, which will evict older entries. This means: do not dedicate all available memory to OHC.
We recommend using jemalloc to keep fragmentation low. On Unix operating systems, preload jemalloc.
OSX usually does not require jemalloc for performance reasons. Also make sure that you are using a recent version of jemalloc - some Linux distributions still provide quite old versions.
To preload jemalloc on Linux, use
export LD_PRELOAD=<path-to-libjemalloc.so, to preload jemalloc on OSX, use
export DYLD_INSERT_LIBRARIES=<path-to-libjemalloc.so. A script template for preloading can be found at the
Apache Cassandra project.
Quickstart:
OHCache ohCache = OHCacheBuilder.newBuilder()
.keySerializer(yourKeySerializer)
.valueSerializer(yourValueSerializer)
.build();
This quickstart uses the very least default configuration:
- total cache capacity of 64MB or 16 * number-of-cpus, whichever is smaller
- number of segments is 2 * number of cores
- 8192 buckets per segment
- load factor of .75
- your custom key serializer
- your custom value serializer
- no maximum serialized cache entry size
See javadoc of CacheBuilder for a complete list of options.
Key and value serializers need to implement the CacheSerializer interface. This interface has three methods:
int serializedSize(T t)to return the serialized size of the given objectvoid serialize(Object obj, DataOutput out)to serialize the given object to the data outputT deserialize(DataInput in)to deserialize an object from the data input
Clone the git repo to your local machine. Either use the stable master branch or a release tag.
git clone https://github.com/snazy/ohc.git
You need OpenJDK 11 or newer to build from source. Just execute
mvn clean install
You need to build OHC from source because the big benchmark artifacts are not uploaded to Maven Central.
Execute java -jar ohc-benchmark/target/ohc-benchmark-0.7.1-SNAPSHOT.jar -h (when building from source)
to get some help information.
Generally the benchmark tool starts a bunch of threads and performs _get_ and _put_ operations concurrently using configurable key distributions for _get_ and _put_ operations. Value size distribution also needs to be configured.
Available command line options:
-cap <arg> size of the cache -d <arg> benchmark duration in seconds -h help, print this command -lf <arg> hash table load factor -r <arg> read-write ration (as a double 0..1 representing the chance for a read) -rkd <arg> hot key use distribution - default: uniform(1..10000) -sc <arg> number of segments (number of individual off-heap-maps) -t <arg> threads for execution -vs <arg> value sizes - default: fixed(512) -wkd <arg> hot key use distribution - default: uniform(1..10000) -wu <arg> warm up - <work-secs>,<sleep-secs> -z <arg> hash table size -cs <arg> chunk size - if specified it will use the "chunked" implementation -fks <arg> fixed key size in bytes -fvs <arg> fixed value size in bytes -mes <arg> max entry size in bytes -unl do not use locking - only appropiate for single-threaded mode -hm <arg> hash algorithm to use - MURMUR3, XX, CRC32 -bh show bucket historgram in stats -kl <arg> enable bucket histogram. Default: false
Distributions for read keys, write keys and value sizes can be configured using the following functions:
EXP(min..max) An exponential distribution over the range [min..max] EXTREME(min..max,shape) An extreme value (Weibull) distribution over the range [min..max] QEXTREME(min..max,shape,quantas) An extreme value, split into quantas, within which the chance of selection is uniform GAUSSIAN(min..max,stdvrng) A gaussian/normal distribution, where mean=(min+max)/2, and stdev is (mean-min)/stdvrng GAUSSIAN(min..max,mean,stdev) A gaussian/normal distribution, with explicitly defined mean and stdev UNIFORM(min..max) A uniform distribution over the range [min, max] FIXED(val) A fixed distribution, always returning the same value Preceding the name with ~ will invert the distribution, e.g. ~exp(1..10) will yield 10 most, instead of least, often Aliases: extr, qextr, gauss, normal, norm, weibull
(Note: these are similar to the Apache Cassandra stress tool - if you know one, you know both ;)
Quick example with a read/write ratio of .9, approx 1.5GB max capacity, 16 threads that runs for 30 seconds:
java -jar ohc-benchmark/target/ohc-benchmark-0.5.1-SNAPSHOT.jar
(Note that the version in the jar file name might differ.)
On a 2.6GHz Core i7 system (OSX) the following numbers are typical running the above benchmark (.9 read/write ratio):
- # of gets per second: 2500000
- # of puts per second: 270000
When using a very huge number of objects in a very large heap, Virtual machines will suffer from increased GC pressure since it basically has to inspect each and every object whether it can be collected and has to access all memory pages. A cache shall keep a hot set of objects accessible for fast access (e.g. omit disk or network roundtrips). The only solution is to use native memory - and there you will end up with the choice either to use some native code (C/C++) via JNI or use direct memory access.
Native code using C/C++ via JNI has the drawback that you have to naturally write C/C++ code for each and every platform. Although most Unix OS (Linux, OSX, BSD, Solaris) are quite similar when dealing with things like compare-and-swap or Posix libraries, you usually also want to support the other platform (Windows).
Both native code and direct memory access have the drawback that they have to "leave" the JVM "context" - want to say that access to off heap memory is slower than access to data in the Java heap and that each JNI call has some "escape from JVM context" cost.
But off heap memory is great when you have to deal with a huge amount of several/many GB of cache memory since that dos not put any pressure on the Java garbage collector. Let the Java GC do its job for the application where this library does its job for the cached data.
TL;DR allocating off-heap memory directly and bypassing ByteBuffer.allocateDirect is very gentle to the
GC and we have explicit control over memory allocation and, more importantly, free. The stock implementation
in Java frees off-heap memory during a garbage collection - also: if no more off-heap memory is available, it
likely triggers a Full-GC, which is problematic if multiple threads run into that situation concurrently since
it means lots of Full-GCs sequentially. Further, the stock implementation uses a global, synchronized linked
list to track off-heap memory allocations.
This is why OHC allocates off-heap memory directly and recommends to preload jemalloc on Linux systems to improve memory managment performance.
OHC was developed in 2014/15 for Apache Cassandra 2.2 and 3.0 to be used as the new row-cache backend.
Since there were no suitable fully off-heap cache implementations available, it has been decided to build a completely new one - and that's OHC. But it turned out that OHC alone might also be usable for other projects - that's why OHC is a separate library.
A big 'thank you' has to go to Benedict Elliott Smith and Ariel Weisberg from DataStax for their very useful input to OHC!
Ben Manes, the author of Caffeine, the highly configurable on-heap cache using W-Tiny LFU.
Developer: Robert Stupp
Copyright (C) 2014 Robert Stupp, Koeln, Germany, robert-stupp.de
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.