Description
Wallaroo is a fast, elastic data processing engine that rapidly takes you from prototype to production by eliminating infrastructure complexity.
Wallaroo alternatives and similar packages
Based on the "Concurrency and Parallelism" category.
Alternatively, view Wallaroo alternatives based on common mentions on social networks and blogs.
-
Ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. -
SCOOP (Scalable COncurrent Operations in Python)
SCOOP (Scalable COncurrent Operations in Python) -
concurrent.futures
(Python standard library) A high-level interface for asynchronously executing callables. -
multiprocessing
(Python standard library) Process-based "threading" interface.
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README
What is Wally?
Wally is a fast stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler.
When we set out to build Wally, we had several high-level goals in mind:
- Create a dependable and resilient distributed computing framework
- Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic
- Provide high-performance & low-latency data processing
- Be portable and deploy easily (i.e., run on-prem or any cloud)
- Manage in-memory state for the application
- Allow applications to scale as needed, even when they are live and up-and-running
Getting Started
Wally can be installed via our handy Wallaroo Up command. Check out our installation page to learn more.
APIs
The primary API for Wally is written in Pony. Wally applications are written using this Pony API.
Usage
Once you've installed Wally, Take a look at some of our examples. A great place to start are our [word_count][word_count] or [market spread][market-spread] examples in [Pony](pony-examples).
"""
Word Count App
"""
use "assert"
use "buffered"
use "collections"
use "net"
use "serialise"
use "wallaroo_labs/bytes"
use "wallaroo"
use "wallaroo_labs/logging"
use "wallaroo_labs/mort"
use "wallaroo_labs/time"
use "wallaroo/core/common"
use "wallaroo/core/metrics"
use "wallaroo/core/sink/tcp_sink"
use "wallaroo/core/source"
use "wallaroo/core/source/tcp_source"
use "wallaroo/core/state"
use "wallaroo/core/topology"
actor Main
new create(env: Env) =>
Log.set_defaults()
try
let pipeline = recover val
let lines = Wallaroo.source[String]("Word Count",
TCPSourceConfig[String].from_options(StringFrameHandler,
TCPSourceConfigCLIParser("Word Count", env.args)?, 1))
lines
.to[String](Split)
.key_by(ExtractWord)
.to[RunningTotal](AddCount)
.to_sink(TCPSinkConfig[RunningTotal].from_options(
RunningTotalEncoder, TCPSinkConfigCLIParser(env.args)?(0)?))
end
Wallaroo.build_application(env, "Word Count", pipeline)
else
env.err.print("Couldn't build topology")
end
primitive Split is StatelessComputation[String, String]
fun name(): String => "Split"
fun apply(s: String): Array[String] val =>
let punctuation = """ !"#$%&'()*+,-./:;<=>[email protected][\]^_`{|}~ """
let words = recover trn Array[String] end
for line in s.split("\n").values() do
let cleaned =
recover val s.clone().>lower().>lstrip(punctuation)
.>rstrip(punctuation) end
for word in cleaned.split(punctuation).values() do
words.push(word)
end
end
consume words
class val RunningTotal
let word: String
let count: U64
new val create(w: String, c: U64) =>
word = w
count = c
class WordTotal is State
var count: U64
new create(c: U64) =>
count = c
primitive AddCount is StateComputation[String, RunningTotal, WordTotal]
fun name(): String => "Add Count"
fun apply(word: String, state: WordTotal): RunningTotal =>
state.count = state.count + 1
RunningTotal(word, state.count)
fun initial_state(): WordTotal =>
WordTotal(0)
primitive StringFrameHandler is FramedSourceHandler[String]
fun header_length(): USize =>
4
fun payload_length(data: Array[U8] iso): USize ? =>
Bytes.to_u32(data(0)?, data(1)?, data(2)?, data(3)?).usize()
fun decode(data: Array[U8] val): String =>
String.from_array(data)
primitive ExtractWord
fun apply(input: String): Key =>
input
primitive RunningTotalEncoder
fun apply(t: RunningTotal, wb: Writer = Writer): Array[ByteSeq] val =>
let result =
recover val
String().>append(t.word).>append(", ").>append(t.count.string())
.>append("\n")
end
wb.write(result)
wb.done()
Documentation
Are you the sort who just wants to get going? Dive right into our documentation then! It will get you up and running with Wally.
Wally currently exists as a mono-repo. All the source that is Wally is located in this repo. See [repo directory structure][repo-directory-structure-link] for more information.
You can also take a look at our FAQ.
Need Help?
Trying to figure out how to get started? Drop us a line:
Contributing
We welcome contributions. Please see our [Contribution Guide][contribution-guide]
For your pull request to be accepted you will need to accept our Contributor License Agreement
License
Wally is licensed under the Apache version 2 license.
*Note that all licence references and agreements mentioned in the Wallaroo README section above
are relevant to that project's source code only.