store

The 'store' package provides efficient binary serialization. There are a couple features that particularly distinguish it from most prior Haskell serialization libraries:

• Its primary goal is speed. By default, direct machine representations are used for things like numeric values (Int, Double, Word32, etc) and buffers (Text, ByteString, Vector, etc). This means that much of serialization uses the equivalent of memcpy.

  We have plans for supporting architecture independent serialization - see #36 and #31. This plan makes little endian the default, so that the most common endianness has no overhead.

  • Another way that the serialization behavior can vary is if integer-simple is used instead of GHC's default of using GMP. Integer serialized with the integer-simple flag enabled are not compatible with those serialized without the flag enabled.

• Instead of implementing lazy serialization / deserialization involving multiple input / output buffers, peek and poke always work with a single buffer. This buffer is allocated by asking the value for its size before encoding. This simplifies the encoding logic, and allows for highly optimized tight loops.

• store can optimize size computations by knowing when some types always use the same number of bytes. This allows us to compute the byte size of a Vector Int32 by just doing length v * 4.

It also features:

• Optimized serialization instances for many types from base, vector, bytestring, text, containers, time, template-haskell, and more.

• TH and GHC Generics based generation of Store instances for datatypes.

• TH generation of testcases.

• Utilities for streaming encoding / decoding of Store encoded messages, via the store-streaming package.

Gotchas

Store is best used for communication between trusted processes and local caches. It can certainly be used for other purposes, but the builtin set of instances have some gotchas to be aware of:

• Store's builtin instances serialize in a format which depends on machine endianness.

• Store's builtin instances trust the data when deserializing. For example, the deserialization of Vector will read the vector's length from the first 8 bytes. It will then allocate enough memory to store all the elements. Malicious or malformed input could cause allocation of large amounts of memory. See issue #122.

Blog posts

• Initial release announcement
• Benchmarks of the prototype
• New 'weigh' allocation benchmark package, created particularly to aid optimizing store.