flatparse is a high-performance parsing library, supporting parsing for programming languages, human-readable data and machine-readable data. The "flat" in the name refers to the
ByteString parsing input, which has pinned contiguous data, and also to the library internals, which avoids indirections and heap allocations whenever possible.
flatparse is generally lower-level than
parsec-style libraries, but it is possible to build higher-level features (such as source spans, hints, indentation parsing) on top of it, without making any compromises in performance.
It is advised to build with
when using this package, since that can result in significant speedups (20-40%
from what I've seen). Additionally, you can enable
specifically by enabling the
llvm package flag. However, this has minor
impact, since almost all parser code will be typically inlined into modules
flatparse, and compiled there.
Features and non-features
- Excellent performance. On microbenchmarks,
flatparse is around 10 times faster than
megaparsec. On larger examples with heavier use of source positions and spans and/or indentation parsing, the performance difference grows to 20-30 times. Compile times and exectuable sizes are also significantly better with
flatparse than with
flatparse internals make liberal use of unboxed tuples and GHC primops. As a result, pure validators (parsers returning
flatparse are not difficult to implement with zero heap allocation.
- No incremental parsing, and only strict
ByteString is supported as input. However, it can be still useful to convert from
String or other types to
ByteString, and then use
flatparse for parsing, since
flatparse performance usually more than makes up for the conversion costs.
- Only little-endian 64 bit systems are currently supported as the host machine. This may change in the future. Getting good performance requires architecture-specific optimizations; I've only considered the most common setting at this point. However,
flatparse does include specific big-endian parsers for primitive integer types.
- Support for fast source location handling, indentation parsing and informative error messages.
flatparse provides a low-level interface to these. Batteries are not included, but it should be possible for users to build custom solutions, which are more sophisticated, but still as fast as possible. In my experience, the included batteries in other libraries often come with major unavoidable overheads, and often we still have to extend existing machinery in order to scale to production features.
- The backtracking model of
flatparse is different to parsec libraries, and is more close to the nom library in Rust. The idea is that parser failure is distinguished from parsing error. The former is used for control flow, and we can backtrack from it. The latter is used for unrecoverable errors, and by default it's propagated to the top.
flatparse does not track whether parsers have consumed inputs. In my experience, what we really care about is the failure/error distinction, and in
megaparsec the consumed/non-consumed separation is often muddled and discarded in larger parser implementations. By default, basic
flatparse parsers can fail but can not throw errors, with the exception of the specifically error-throwing operations. Hence,
flatparse users have to be mindful about grammar, and explicitly insert errors where it is known that the input can't be valid.
flatparse comes in two flavors:
FlatParse.Stateful. Both support a custom error type.
FlatParse.Basic only supports the above features. If you don't need indentation
parsing, this is sufficient.
FlatParse.Stateful additionally supports a built-in
Int worth of internal state
and an additional custom reader environemnt. This can support a wide range of indentation parsing
features. There is a slight overhead in performance and code size compared to
Basic. However, in
small parsers and microbenchmarks the difference between
Stateful is often reduced
to near zero by GHC and/or LLVM optimization.
Informative tutorials are work in progress. See
for a lexer/parser example with acceptably good error messages.
Pull requests are welcome. I'm fairly quick to add PR authors as collaborators.
Execution times below. See source code in bench. Compiled with GHC
-O2 -fllvm. Executed on Intel 1165G7 CPU at 28W power draw.
Object file sizes for each module containing the
long keyword and
numeral csv benchmarks.
||object file size (bytes)