What is Chronicle Map?
Chronicle Map works like a standard java map yet it automatically distributes data between processes, these processes can be both on the same server or across your network. In other words its a low latency, huge data key-value store, which can store terabytes of data locally to your process.
It is able to access this data with sub-microsecond latency. Chronicle is built on Java but it does not undergo, the garbage collection pauses, which most Java applications experience. This is because chronicle stores your data off-heap. Even if you have a large cache with lots of updates, the garbage collector will have almost no effect; resulting in more predictable latency and throughput. The off-heap data is stored in a memory mapped file, which offers further resilience to unforeseen outages.
Chronicle Map Features
How does it work?
You define the structure of the entries you wish to store and set the maximum number of entries. Chronicle will create an appropriately sized memory mapped file, say 4TB. The size of the memory mapped file is not constrained by physical memory, so unlike heap-based key-value stores, chronicle eliminates the pauses that can occur when the store has to be resized.
If your data grid spans two or more processes on the same server, each process will share the same 4TB space, fine grain locking will ensure that your data integrity. The data is transferred directly using shared memory when replicating on the same server we don’t use TCP/IP loopback, which will add additional latency, we just use memory. This approach gives you outstanding performance, we have measured data being transferred between processes in a staggering 40 nanoseconds.
Chronicle Map and Scalability
Chronicle Map has been tested to do 30 million updates per second with 500 million key-values on a 16 core machine, with a 64 MB heap and no minor collections.
Supporting multiway and multi-mastering and 1.6 million updates per second replicated across a low bandwidth between two laptops.
A ping-pong test between processes of 25 million per second. One process set a flag from false to true, the other from true to false. Data is not lost even if the JVM crashes on the next line.
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