Early development stage: this project was still under early development, many necessery feature was not done yet, use it on your own risk.
A node.js version of Spark, without hadoop or jvm.
You should read tutorial first, then you can learn Spark but use this project instead.
Any api that requires a RDD and generate a result is async, like count
, take
, max
...
Any api that creates a RDD is deferred API, which is not async, so you can chain them like this:
await dcc
.parallelize([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
.map(v => v + 1)
.filter(v => v % 2 === 0)
.take(10); // take is not deferred api but async
- [x] local master.
- [x] rdd & partition creation & release.
- [x] map & reduce
- [x] repartition & reduceByKey
- [x] disk storage partitions
- [x] cache
- [x] file loader & saver
- [x] export module to npm
- [x] decompresser & compresser
- [x] use debug module for information/error
- [x] provide a progress bar.
- [ ] sampler
- [x] sort
- [ ] object hash(for key) method
- [ ] storage MEMORY_OR_DISK, and use it in sort
- [ ] storage MEMORY_SER,storage in memory but off v8 heap.
- [ ] config default partition count.
- [ ] distributed master
- [ ] runtime sandbox
- [ ] plugin system
- [ ] remote dependency management
- [ ] aliyun oss loader
- [ ] hdfs loader
npm install -g dcf
#or
yarn global add dcf
Then you can use command: dcf-shell
npm install --save dcf
#or
yarn add dcf
Then you can use dcf with javascript or typescript.
download this repo, install dependencies
npm install
# or
yarn
Run samples:
npm run ts-node src/samples/tutorial-0.ts
npm run ts-node src/samples/repartition.ts
Run interactive cli:
npm start