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https://github.com/cnuernber/charred

zero dependency efficient read/write of json and csv data.
https://github.com/cnuernber/charred

clojure csv java json

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zero dependency efficient read/write of json and csv data.

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# Charred



Efficient character-based file parsing for csv and json formats.



[![Clojars Project](https://clojars.org/com.cnuernber/charred/latest-version.svg)](https://clojars.org/com.cnuernber/charred)



* Zero dependencies.

* As fast as univocity or jackson.

* Same API as clojure.data.csv and clojure.data.json implemented far more

  efficiently.



* [API Documentation](https://cnuernber.github.io/charred/)

* [Simple JSON benchmarks](https://github.com/cnuernber/fast-json)

* [Large CSV Support](https://cnuernber.github.io/charred/charred.bulk.html#var-batch-csv-rows)



## Usage



```clojure

user> (require '[charred.api :as charred])

nil

user> (charred/read-json "{\"a\": 1, \"b\": 2}")

{"a" 1, "b" 2}

user> (charred/read-json "{\"a\": 1, \"b\": 2}" :key-fn keyword)

{:a 1, :b 2}

user> (println (charred/write-json-str *1))

{

  "a": 1,

  "b": 2

}

```



#### A Note About Efficiency



If you are reading or writing a lot of small JSON objects the best option is to create a

[specialized parse fn](https://cnuernber.github.io/charred/charred.api.html#var-parse-json-fn)

to exactly the options that you need and pass in strings or char[] data.  A [similar pathway](https://cnuernber.github.io/charred/charred.api.html#var-write-json-fn) exists for high

performance writing of json objects.  The returned functions are safe to use in multithreaded

contexts.



The system is overall tuned for large files.  Small files or input streams should be setup with `:async?` false

and smaller `:bufsize` arguments such as 8192 as there is no gain for async loading when the file/stream is smaller than 1MB.

For smaller streams slurping into strings in an offline threadpool will lead to the highest performance.  For a particular

file size if you know you are going to parse many of these then you should gridsearch `:bufsize` and `:async?` as

that is a tuning pathway that I haven't put a ton of time into.  In general the system is tuned towards larger

files as that is when performance really does matter.



All the parsing systems have mutable options.  These can be somewhat faster and it is interesting to

look at the tradeoffs involved.  Parsing a csv using the raw supplier interface is a bit faster

than using the Clojure sequence pathway into persistent vectors and it probably doesn't really

change your consume pathway so it may be worth trying it.



## Development



Before running a REPL you must compile the java files into target/classes.  This directory

will then be on your classpath.



```console

scripts/compile

```



Tests can be run with `scripts/run-tests` which will compile the java and then run the tests.



## Lies, Damn Lies, and Benchmarks!



See the [fast-json project](https://github.com/cnuernber/fast-json/blob/master/src/fjson.clj#L247).  These times are for 

parsing a 100k json document using keywords for map keys - `:key-fn keyword`.



#### Intel JDK-8



|       method | performance µs |

|--------------|---------------:|

|    data.json |           4275 |

|     jsonista |            754 |

|      charred |            638 |

| charred-hamf |            486 |



#### Intel JDK-19



|       method | performance µs |

|--------------|---------------:|

|    data.json |           5608 |

|     jsonista |            856 |

|      charred |            673 |

| charred-hamf |            531 |



#### Mac m-1 JDK-19



|        method | performance µs |

|---------------|---------------:|

|     data.json |           3164 |

|      jsonista |            285 |

|       charred |            249 |

|  charred-hamf |            227 |



## License



MIT license.