Releases: WallarooLabs/wally
0.6.1
Release Notes
We’re excited to announce the addition of stream windowing to the Wallaroo API. You can now aggregate data in count or range-based windows. For example, the following code sample defines 6-second sliding windows starting every 3 seconds for a user-defined aggregation:
(inputs
.to(wallaroo.range_windows(wallaroo.seconds(6))
.with_slide(wallaroo.seconds(3))
.over(MyAgg))
.to_sink(sink_config))This means first-class support for the wide range of streaming use cases that require windowing.
We've also updated our documentation site! If you happen to run into any problems when navigating the new site, feel free to open an issue!
What is Wallaroo
Wallaroo is a modern, extensible framework that makes it simple to get stateful streaming data and event-driven applications to production fast, regardless of scale.
If you are interested in installing Wallaroo, our installation documentation provides the various ways you can get up and running.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
New Features and Improvements
Aggregations
Aggregations are an alternative to state computations that trade some of the freedom provided by state computations for the ability to efficiently compute results in windows. This goes hand-in-hand with our new Windowing API, which is also part of this release.
Windowing API
Wallaroo now supports windowing over aggregations. This allows you to break an infinite stream into manageable chunks and also allows you to see how your inputs evolve over time.
There are two broad categories of windows currently supported: count-based and range-based. Count-based windows emit an output every n input messages, where n is specified via the API. Range-based windows are based on fixed time ranges, and can be further divided into tumbling and sliding (overlapping) varieties. See our documentation for an in-depth description of these concepts and our API.
API Update: collect()
We pass an implicit routing key along each stage in a Wallaroo pipeline. This release adds a new API call ".collect()" for the case where you want all messages after a certain stage to be assigned the same routing key. Prior to this change, you had to use a key_by call that used a constant key_extractor function. This was onerous and also limited our options to optimize topology layouts under the hood.
Installing Wallaroo
There are currently four ways that you can install Wallaroo:
The Wallaroo Up script
Docker
Vagrant
Upgrading Wallaroo
In all cases below, if you run into issues, please reach out to us! We’re available on twitter, IRC, Github, by email, our mailing list, or our subreddit. We love questions!
If you have made no changes to Wallaroo or Pony since installation, your best bet will be to start from scratch, following the instructions of your choice.
Below are instructions for Upgrading Wallaroo via Wallaroo Up, Upgrading Wallaroo in Docker, and Upgrading Wallaroo in Vagrant.
Upgrading Wallaroo via Wallaroo Up
The normal Wallaroo Up installation instructions will install new versions next to existing versions.
Upgrading the Wallaroo Docker image
To upgrade the Wallaroo Docker image, run the following command to get the latest image. If you don't allow a non-root user to run Docker commands, you'll need to add sudo to the front of the command.
docker pull wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.1Upgrading Wallaroo Source Code
If you mounted the Wallaroo source code to your local machine using the directory recommended in setup, in /tmp/wallaroo-docker (UNIX & MacOS users) or c:/wallaroo-docker (Windows users), then you will need to move the existing directory in order to get the latest source code. The latest Wallaroo source code will be copied to this directory automatically when a new container is started with the latest Docker image.
UNIX & MacOS Users
For UNIX users, you can move the directory with the following command:
mv /tmp/wallaroo-docker/wallaroo-src/ /tmp/wallaroo-docker/wallaroo-0.6.0-src/Windows Users
For Windows users, you can move the directory with the following command:
move c:/wallaroo-docker/wallaroo-src/ c:/wallaroo-docker/wallaroo-0.6.0-srcOnce done moving, you can re-create the wallaroo-src directory with the following command:
mkdir c:\wallaroo-docker\wallaroo-srcUpgrading Wallaroo in Vagrant
To upgrade your Wallaroo installation in Vagrant, you’ll want to follow the latest installation instructions for Wallaroo in Vagrant.
If you have modified your old Vagrant VM in any way that you intend to persist, you should persist your changes now. For example, copy any edited or new files from the old Vagrant VM to the new one.
CHANGELOG
[0.6.1] - 2018-12-31
Added
- Add support for windowing (PR #2735)
0.6.0
Release Notes
We are excited to announce the release of Wallaroo 0.6.0. The most significant change in this release is a complete overhaul of the Wallaroo API to make it cleaner, simpler, and more intuitive. As a result of these changes, this is a breaking release. We also want to thank Github users ChristianWitts and cristaloleg for their contributions to Wallaroo last month!
We would love to hear what you think of the new API and how you plan to use Wallaroo. Please reach out to us! We’re available on Twitter, IRC, GitHub, by email, our mailing list, or our subreddit. We love questions!
What is Wallaroo
Wallaroo is a modern, extensible framework that makes it simple to get stateful streaming data and event-driven applications to production fast, regardless of scale.
If you are interested in installing Wallaroo, our installation documentation provides the various ways you can get up and running.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
- New Features and Improvements
Python 3 Support for Connectors
Connectors API Update
Streamlined Wallaroo Python API - Converting to the New API
- Installing Wallaroo
- Upgrading Wallaroo
- CHANGELOG
New Features and Improvements
Python 3 Support for Connectors
The Connectors API has been updated to work with Python 3.5 and up, and all of the example connectors have been tested against Python 3.5. Prior to this work Connectors would only work under Python 2.7.
Connectors API Update
We’ve made some changes to the connectors API when defining applications and pipelines to bring it more in line with how other built-in sources and sinks are defined. These changes only impact your application_setup code and should not require code changes in the connector scripts.
An example application has been updated in this release and the documentation includes all relevant details if you’re getting started. You’re encouraged to keep reading the section on the streamlined API below as this is all relevant to how your application code should be updated. For quick reference, the new source and sink configuration constructors look like this:
source_config = wallaroo.experimental.SourceConnectorConfig(
"source_name",
encoder=source_encode_function,
decoder=source_decode_function,
port=7100)
sink_config = wallaroo.experimental.SinkConnectorConfig(
"sink_name",
encoder=sink_encode_function,
decoder=sink_decode_function,
port=7200)As before, the names must match what you pass to the connector scripts so the right data flows to each part. Ports are now assigned explicitly and should be unique for each connector. These configuration values can be passed into source and sink pipeline components respectively. Read on for more information on how to use the streamlined pipeline components.
Streamlined Wallaroo Python API
The original Wallaroo Python API has existed in roughly the same form since September 2017. Based on user feedback and continuous internal experimentation, we decided it was time to streamline the API both to create a better developer experience and to allow us to more easily add functionality to Wallaroo in the future. We’re going to describe the new API in isolation in this section. If you want to know how to convert from the old to the new API, see here.
Defining a Simple Wallaroo Application
A Wallaroo application includes one or more sources. You use wallaroo.source(...) to define a stream originating from a source. Each source stream can be followed by one or more computation stages (we will describe how to define computations themselves later on). A linear sequence from a source through zero or more computations constitutes a partial pipeline. For example:
inputs = wallaroo.source("Source Name", source_config)
partial_pipeline = inputs.to(my_computation)This defines a partial pipeline that could be diagrammed as followed:
Source -> my_computation ->
The hanging arrow at the end of this diagram indicates that the pipeline is partial. We can still add more stages, and to complete the pipeline we need one or more sinks.
You create a complete pipeline by terminating a partial pipeline with a call to to_sink or to_sinks. For example:
inputs = wallaroo.source("Source Name", source_config)
complete_pipeline = (inputs
.to(my_computation)
.to_sink(sink_config))Our pipeline is now complete:
Source -> my_computation -> Sink
Unless a call to to using a stateless computation is preceded by a call to key_by (which partitions messages by key), there are no guarantees around the order in which messages will be processed. That's because Wallaroo might parallelize a stateless computation if that is beneficial for scaling. That means the execution graph for the above pipeline could look like this:
/-> my_stateless_computation -\
/ \
Source ----> my_stateless_computation ----> Sink
\ /
\-> my_stateless_computation -/
Some messages will be routed to each of the parallel computation instances. When they merge again at the sink, these messages will be interleaved in a non-deterministic fashion.
Merging Partial Pipelines
You can merge two partial pipelines to form a new partial pipeline. For example:
inputs1 = wallaroo.source("Source 1", source_config)
partial_pipeline1 = inputs1.to(computation1)
inputs2 = wallaroo.source("Source 2", source_config)
partial_pipeline2 = inputs2.to(computation2)
partial_pipeline = inputs1.merge(inputs2)The resulting partial pipeline could be
Source1 -> computation1 ->\
\
->
/
Source2 -> computation2 ->/
Again, the hanging arrow indicates we can still add more stages, and that to complete the pipeline we still need one or more sinks. You could also merge this partial pipeline with additional partial pipelines. When you merge partial pipelines in this way, you are not creating a join in the sense familiar from SQL joins. Instead, you are combining two streams into one, with messages from the first stream interwoven with messages from the second. That combined stream is then passed to the next stage following the hanging arrow.
The following is an example of a complete pipeline including a merge where we first add one more computation before the sink:
pipeline = (inputs1.merge(inputs2)
.to(computation3)
.to_sink(sink_config))The corresponding diagram for this definition would look like this:
Source1 -> computation1 ->\
\
-> computation3 -> Sink
/
Source2 -> computation2 ->/
Building an Application
Once you have defined a complete pipeline, you must pass it into wallaroo.build_application(app_name, pipeline) in order to build the application object you must return from the application_setup function.
For a simple application with a decoder, computation, and encoder, the application_setup function might look like
def application_setup(args):
inputs = wallaroo.source("Source Name", source_config)
pipeline = (inputs
.to(computation)
.to_sink(sink_config))
return wallaroo.build_application("Application Name", pipeline)Defining Computations
There are two types of computations that can be added to a Wallaroo pipeline: stateless and state computations. The API for stateless computation has not changed, so we will only discuss state computations here.
A state computation takes an input message and a state object, does some work which might involve updating that state, and then optionally returns an output that will be sent downstream. Here is an example of a simple state computation taken from our Word Count example:
class WordTotal(object):
def __init__(self):
self.count = 0
@wallaroo.state_computation(name="count word", state=WordTotal)
def count_word(word, word_total):
word_total.count = word_total.count + 1
return WordCount(word, word_total.count)The count_word function takes an input called word and the state representing the running total called word_total. We specify the associated state class by passing WordTotal as the decor...
0.5.4
Release Notes
We are excited to announce the release of Wallaroo 0.5.4. The highlight of 0.5.4 is support for Python 3. Users can now use machida3 to develop Wallaroo applications written in Python 3, see our latest documentation to get started. Although this is a preview release, we are very excited to get it into your hands.
This is a patch release, meaning there are no breaking changes to the existing API. This allows you to drop in your existing Python 2 application(s) into the latest release and take full advantage of bug fixes we’ve made since our last release.
We wanted to give a special shoutout to Github user caj-larsson for their contributions and for getting the ball rolling with “support for Python 3” development. We also wanted to thank Github user voxadam for their contribution to fixing a link in our README. Read on for more details about Python 3 support and other changes that happened with this release.
We would love to hear what you think and how you plan to use Wallaroo with Python 3. Please reach out to us! We’re available on Twitter, IRC, GitHub, by email, our mailing list,
or our subreddit. We love questions!
What is Wallaroo
Wallaroo is a modern, extensible framework that makes it simple to get stateful streaming data and event-driven applications to production fast, regardless of scale.
If you are interested in installing Wallaroo, our installation documentation provides the various ways you can get up and running.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
Added
Python 3 Support
We have added support for running Wallaroo applications that are written using Python 3. This means that you will be able to use Python 3 features and libraries when creating your applications.
The Wallaroo API remains the same for Python 3. In order to run Python 3 applications, you must use a new executable called machida3 instead of machida.
If you are interested in updating your existing Wallaroo code to use Python 3, you should do the following:
Port your code from Python 2 to Python 3.
Make sure that the encoders and partition functions return Python strings or byte arrays. In Python 2.7 some string operations (such as [...]) returned strings while in Python 3 the same operation returns a character.
Support for Python 3 has been tested to work with Python 3.5 on our Docker image, our Vagrant box, Ubuntu (Xenial, Artful, Bionic) and Debian (Stretch, Buster).
This is a preview release of the Python 3 support and may change based on feedback. Please share your thoughts, we’re available on Twitter, IRC, GitHub, by email, our mailing list,
or our subreddit.
Changed
Fedora 26 no Longer Supported
Due to Fedora 26 having reached its EOL this year and the lack of support for the latest tooling needed to run Wallaroo, we have dropped support for Fedora 26 with this release. It should be noted that you can still run Wallaroo 0.5.3 if you wish and that we continue to support Fedora 27/28.
Fixed
Upgrading Wallaroo
In all cases below, if you run into issues, please reach out to us! We’re available on twitter, IRC, Github, by email, our mailing list,
or our subreddit.
We love questions!
If you have made no changes to Wallaroo or Pony since installation, your best bet will be to start from scratch, following the instructions of your choice.
Below are instructions for Upgrading Wallaroo via Wallaroo Up, Upgrading Wallaroo in Docker, Upgrading Wallaroo in Vagrant, and Upgrading Wallaroo when compiled from source.
Upgrading Wallaroo via Wallaroo Up
The normal Wallaroo Up installation instructions will install new versions next to existing versions.
Upgrading the Wallaroo Docker image
To upgrade the Wallaroo Docker image, run the following command to get the latest image. If you don't allow a non-root user to run Docker commands, you'll need to add sudo to the front of the command.
docker pull wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.5.4Upgrading Wallaroo Source Code
If you mounted the Wallaroo source code to your local machine using the directory recommended in setup, in /tmp/wallaroo-docker (UNIX & MacOS users) or c:/wallaroo-docker (Windows users), then you will need to move the existing directory in order to get the latest source code. The latest Wallaroo source code will be copied to this directory automatically when a new container is started with the latest Docker image.
UNIX & MacOS Users
For UNIX users, you can move the directory with the following command:
mv /tmp/wallaroo-docker/wallaroo-src/ /tmp/wallaroo-docker/wallaroo-0.5.3-src/Windows Users
For Windows users, you can move the directory with the following command:
move c:/wallaroo-docker/wallaroo-src/ c:/wallaroo-docker/wallaroo-0.5.3-srcOnce done moving, you can re-create the wallaroo-src directory with the following command:
mkdir c:\wallaroo-docker\wallaroo-srcUpgrading Wallaroo in Vagrant
To upgrade your Wallaroo installation in Vagrant, you’ll want to follow the latest installation instructions for Wallaroo in Vagrant.
If you have modified your old Vagrant VM in any way that you intend to persist, you should persist your changes now. For example, copy any edited or new files from the old Vagrant VM to the new one.
Upgrading Wallaroo when compiled from source
These instructions are for Ubuntu Linux. It's assumed that if you are using a different operating system then you are able to translate these instructions to your OS of choice.
Upgrading ponyc to 0.25.0
ponyc can be upgraded with the following command:
sudo apt-get install --only-upgrade ponyc=0.25.0Verify you are now on the correct version of ponyc by running:
ponyc --versionYou should get the following output:
0.25.0 [release]How to Upgrade Wallaroo
Once you're on the latest ponyc and pony stable, you're ready to switch over to Wallaroo 0.5.4.
If you have made prior changes to the Wallaroo code, you’ll need to re-implement those changes. To get the latest release, assuming that you previously installed to the directory we recommended in setup, you’ll need to run the following:
cd ~/wallaroo-tutorial/To get a new copy of the Wallaroo repository, run the following commands:
cd ~/wallaroo-tutorial/
curl -L -o wallaroo-0.5.4.tar.gz 'https://wallaroo-labs.bintray.com/wallaroolabs-ftp/wallaroo/0.5.4/wallaroo-0.5.4.tar.gz'
mkdir wallaroo-0.5.4
tar -C wallaroo-0.5.4 --strip-components=1 -xzf wallaroo-0.5.4.tar.gz
rm wallaroo-0.5.4.tar.gz
cd wallaroo-0.5.4You can then run the following commands to build the necessary tools to continue developing using Wallaroo 0.5.4:
cd ~/wallaroo-tutorial/wallaroo-0.5.4
make build-machida build-machida3 build-giles-all build-utils-cluster_shutdownCHANGELOG
[0.5.4] - 2018-10-31
Added
Added Python3 support for Wallaroo in our Docker image and in installations via Vagrant, Wallaroo Up (on Ubuntu (Xenial, Artful, and Bionic) and Debian (Stretch and Buster)), as well as for installation from source on systems with Python 3.5 or higher (python3-dev is also required).
Changed
Deprecate giles receiver in favor of data receiver (PR #2341)
0.5.3
Release Notes
This is a patch release that includes two very important new features. First, we've released a preview version of the Python Connector API. This allows developers to build sources and sinks without the need to worry about Wallaroo’s internal protocol. We also have a better resilience story: we now use an algorithm based on the Chandy-Lamport snapshotting algorithm that minimizes the impact of checkpointing on processing in-flight messages. Read on for more details about each feature and other fixes that happened with this release.
We would love to hear what you think and how you plan to use these new features. Please reach out to us! We’re available on Twitter, IRC, GitHub, by email, or our mailing list. We love questions!
What is Wallaroo
Wallaroo is a modern, extensible framework that makes it simple to get stateful streaming data and event-driven applications to production fast, regardless of scale.
If you are interested in installing Wallaroo, our installation documentation provides the various ways you can get up and running.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
License changes
Starting with this release (version 0.5.3), Wallaroo is now licensed completely under an Apache2 license. If you aren’t familiar with the Apache2 license you can find it here.
Added
Python Connector API
The Python Connector API provides developers with a way to quickly and easily connect their data streams as sources and sinks to Wallaroo with a minimal amount of code. Python connectors are processes that run outside of Wallaroo and act as a bridge between Wallaroo and the systems that store data. The API is written in Python, so developers can use the same language for creating connectors and Wallaroo applications.
In addition to the API, we have created connectors for Kafka, Redis, S3, RabbitMQ, Kinesis, Postgres, and UDP. Developers can use these connectors directly or they can use them as a base for building connectors that fit their specific needs.
For more information, please refer to the documentation.
This is a preview release of the connector API and may change based on feedback. Please share your thoughts at [email protected].
Changes
Resilience/Recovery Changes
Checkpointing and Recovery from Checkpoints
We've redesigned and improved our resilience strategy from the ground up. We now use an algorithm based on the Chandy-Lamport snapshotting algorithm that minimizes the impact of checkpointing on processing in-flight messages. A checkpoint represents a consistent recovery line. This means that when a failed worker recovers, we can roll back the cluster to the last checkpoint and begin processing again with the guarantee that all state in the system is valid. The interval between checkpoints is configurable.
One pleasant side effect of this work is that we can now use barriers to determine when all in-flight messages are done processing, which is useful for scenarios like growing and shrinking the running cluster size. This replaces our earlier watermark-based strategy that required acks to be propagated from the sinks back up through the entire upstream chain.
Replicated Recovery Data
This release adds a foundation for building a Wallaroo cluster that can recover from catastrophic file system data loss. One cause of such catastrophic data loss could be the accidental destruction of an Amazon AWS/Google GCE/Azure cloud server instance by the administrator.
Command line arguments are now available to add I/O journalling (i.e., a write-ahead log to a remote file service) to all Wallaroo data written to the --resilience-dir directory.
Wallaroo Up additional distributions
Wallaroo Up, our shell script that automates the from-source install of Wallaroo on multiple Linux distributions now officially supports more distributions (Fedora 26/27, Amazon Linux 2, Oracle Linux, Ubuntu Artful, and Debian Jessie/Buster).
Wallaroo Up now officially supports and has been tested on:
Ubuntu Trusty
Ubuntu Xenial
Ubuntu Artful
Ubuntu Bionic
Fedora 26
Fedora 27
Fedora 28
CentOS 7
Amazon Linux 2
Oracle Linux 7
Debian Jessie
Debian Stretch
Debian Buster (Testing)
Additionally, Wallaroo Up hasn't been tested on but should work on:
Red Hat Enterprise Linux 7
Upgrading Wallaroo
Below are instructions for upgrading from Wallaroo 0.5.1 for Upgrading Wallaroo when compiled from source, Upgrading Wallaroo when installed via Wallaroo Up, Upgrading Wallaroo in Docker, and Upgrading Wallaroo in Vagrant.
Upgrading Wallaroo when compiled from source
Starting with Wallaroo 0.5.2, Wallaroo is installed into a version specific directory. Installations of new versions are installed next to existing versions.
You should follow the normal instructions for installing from source for Wallaroo Python and Wallaroo Go. You can then port over any changes you’ve made to the new version as you see fit.
Upgrading Wallaroo when installed via Wallaroo Up
Wallaroo Up installs Wallaroo into a version specific directory. Installations of new versions are installed next to existing versions. You can then port over any changes you’ve made to the new version as you see fit.
You should follow the normal instructions for installing from Wallaroo Up for Wallaroo Python and Wallaroo Go. You can then port over any changes you’ve made to the new version as you see fit.
Upgrading the Wallaroo Docker image
To upgrade the Wallaroo Docker image, run the following command to get the latest image. If you don't allow a non-root user to run Docker commands, you'll need to add sudo to the front of the command.
docker pull wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.5.3Upgrading Wallaroo Source Code
If you mounted the Wallaroo source code to your local machine using the directory recommended in setup, in /tmp/wallaroo-docker (UNIX & MacOS users) or c:/wallaroo-docker (Windows users), then you will need to move the existing directory in order to get the latest source code. The latest Wallaroo source code will be copied to this directory automatically when a new container is started with the latest Docker image.
UNIX & MacOS Users
For UNIX users, you can move the directory with the following command:
mv /tmp/wallaroo-docker/wallaroo-src/ /tmp/wallaroo-docker/wallaroo-0.5.1-src/Windows Users
For Windows users, you can move the directory with the following command:
move c:/wallaroo-docker/wallaroo-src/ c:/wallaroo-docker/wallaroo-0.5.1-srcOnce done moving, you can re-create the wallaroo-src directory with the following command:
mkdir c:\wallaroo-docker\wallaroo-srcUpgrading Wallaroo in Vagrant
The normal Wallaroo installation in Vagrant instructions will install new versions next to existing versions.
You should follow the normal instructions for installing from Wallaroo via Vagrant for Wallaroo Python and Wallaroo Go.
If you have modified your old Vagrant VM in any way that you intend to persist, you’ll need to do that now. For example, copy any edited or new files from the old Vagrant VM to the new one. When you’ve completed that, it’s a good idea to clean up your old Vagrant box, by running:
cd ~/wallaroo-tutorial/wallaroo/vagrant-0.5.1
vagrant destroyCHANGELOG
[0.5.3] - 2018-09-28
Fixed
- Python's argparse and other libraries which require properly initialized python arguments should no longer fail in certain cases in machida
Added
- Added suppo...
0.5.2
Release Notes
This is a patch level release that expands our available installation options and supported Linux distributions. This release does not include any changes to Wallaroo and so there is no need to upgrade to it from Wallaroo 0.5.1.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
Added
Wallaroo Up
We have introduced a new way to get started with Wallaroo: Wallaroo Up
Wallaroo Up is a shell script that automates the from-source install of Wallaroo on multiple Linux distributions. Wallaroo Up came out of our desire simplify and ease the process of getting started with Wallaroo. We heard from many of you that setting up Wallaroo could be daunting. Wallaroo Up is our first step in towards streamlining the Wallaroo installation process.
Wallaroo Up has been tested on:
- Ubuntu Trusty
- Ubuntu Xenial
- Ubuntu Bionic
- Fedora 28
- CentOS 7
- Debian Stretch
Additionally, Wallaroo Up hasn't been tested on but should work on:
- Ubuntu Artful
- Debian Jessie
- Debian Buster (Testing)
- Red Hat Enterprise Linux 7
- Fedora 26
- Fedora 27
We hope this makes it easier for you to get started with Wallaroo. In all cases above, if you run into issues, please reach out to us! We’re available on twitter, IRC, Github, by email, or our mailing list. We love questions!
For more information on installing Wallaroo Python with Wallaroo Up, take a look at: https://wallaroo-docs-rc.netlify.com/book/getting-started/wallaroo-up.html
For more information on installing Wallaroo Go with Wallaroo Up, take a look at: https://docs.wallaroolabs.com/book/go/getting-started/wallaroo-up.html
Additional Wallaroo Go Environment Support
Vagrant for Wallaroo Go
We have added the ability for you to try out Wallaroo Go using Vagrant (on Windows, Mac and Linux). For more information, take a look at: https://docs.wallaroolabs.com/book/go/getting-started/vagrant-setup.html
Docker for Wallaroo Go
We have added the ability for you to try out Wallaroo Go using Docker (on Windows, Mac and Linux). For more information, take a look at: https://docs.wallaroolabs.com/book/go/getting-started/docker-setup.html
CHANGELOG
[0.5.2] - 2018-08-24
Added
- Added Wallaroo Up to automate development environment setup on multiple Linux distributions
- Added support for Fedora 28, CentOS 7, and Debian Stretch Linux distributions via Wallaroo Up
- Added Vagrant as an option for trying out the Wallaroo Go
- Added Docker as an option for trying out the Wallaroo Go
0.5.1
Release Notes
Hot on the heels of our last release is well...another release! This is a patch level release and includes a minor change and fix we felt were worthy of getting into your hands right away.
We've added a filtering capability to our decoders and fixed an issue which now ensures that parallel stateless computations always run independently.
Feel free to use the table of contents below to help you navigate to sections you might find relevant.
Table of Contents
Changes
Decoder Filtering
Previously, Wallaroo decoders always sent a message to the next step in the pipeline. We realized this wasn't ideal, especially in cases where the decoder receives "bad data". We’ve now added functionality for decoders to filter out a message instead of sending it to the next step. We document exactly how to do so below with examples for both the Python and Go API.
Python API
Both TCPSource decoders and a KafkaSource decoders have been updated to prevent None values from being sent to the next step of the pipeline. Below is an example TCPSource decoder which filters out messages that raise an error while decoding.
Example
Example decoder for a TCPSource
A complete TCPSource decoder example that decodes messages with a 32-bit unsigned integer payload_length and a character followed by a 32-bit unsigned int in its payload. Filters out any input that raises a struct.error by returning None:
@wallaroo.decoder(header_length=4, length_fmt=">I")
def decoder(bs):
try:
return struct.unpack('>1sL', bs)
except struct.error:
return NoneFull decoder documentation can be found in the Python API section of our documentation book.
Go API
Both wallarooapi.FramedDecoders and the wallarooapi.Decoders have been updated to to prevent nil values from being sent to the next step of the pipeline. Below is an example FramedDecoder which filters out messages that raise an error while decoding.
Example
Example FramedDecoder
A complete FramedDecoder example that decodes messages with a 32-bit unsigned integer payload_length and a character followed by a 32-bit unsigned int in its payload. Filters out any data that causes json.Umarshal to return an err by returning nil:
type payload struct {
Letter string
Votes uint32
}
type Decoder struct {}
func (d *Decoder) HeaderLength() uint64 {
return 4
}
func (d *Decoder) PayloadLength(b []byte) uint64 {
return uint64(binary.BigEndian.Uint32(b))
}
func (d *Decoder) Decode(b []byte) interface{} {
var data payload
if err := json.Unmarshal(b, &data); err != nil {
return data
} else {
return nil
}
}Full decoder documentation can be found in the Go API section of our documentation book.
Fixes
Ensure that parallel stateless computations always run independently
Previously, in certain scenarios (such as stateful->stateless->to_parallel), we were coalescing the parallelized stateless computation back on the preceding stateless computation's Producer. This meant all results from the preceding computation went to the same place, defeating the purpose of parallelization.
We now ensure that parallel stateless computations always run independently. No application code changes are needed for this fix to take effect once you've upgraded to Wallaroo 0.5.1.
Upgrading Wallaroo
In all cases below, if you run into issues, please reach out to us! We’re available on twitter, IRC, Github, by email, or our mailing list.
We love questions!
If you have made no changes to Wallaroo or Pony since installation, your best bet will be to delete your Wallaroo installation and start from scratch, following the [instructions] (https://docs.wallaroolabs.com/book/getting-started/choosing-an-installation-option.html) of your choice.
Below are instructions for Upgrading Wallaroo when compiled from source, Upgrading Wallaroo in Docker, and Upgrading Wallaroo in Vagrant.
Upgrading Wallaroo when compiled from source
These instructions are for Ubuntu Linux. It's assumed that if you are using a different operating system then you are able to translate these instructions to your OS of choice.
Upgrading ponyc to 0.24.4
ponyc can be upgraded with the following command:
sudo apt-get install --only-upgrade ponyc=0.24.4Verify you are now on the correct version of ponyc by running:
ponyc --versionYou should get the following output:
0.24.4 [release]How to Upgrade Wallaroo
Once you're on the latest ponyc and pony stable, you're ready to switch over to Wallaroo 0.5.1.
We recommend moving your current Wallaroo directory and starting with a fresh clone of the latest release. If you have made prior changes to the Wallaroo code, you’ll need to re-implement those changes. To get a fresh clone, assuming that you cloned the repository to the directory we recommended in setup, you’ll need to run the following:
cd ~/wallaroo-tutorial/
mv wallaroo/ wallaroo-0.5.0/To get a new copy of the Wallaroo repository, run the following commands:
cd ~/wallaroo-tutorial/
git clone https://github.com/wallaroolabs/wallaroo
cd wallaroo
git checkout 0.5.1You can then run the following commands to build the necessary tools to continue developing using Wallaroo 0.5.1:
cd ~/wallaroo-tutorial/wallaroo
make build-machida build-giles-all build-utils-cluster_shutdownUpgrading the Wallaroo Docker image
To upgrade the Wallaroo Docker image, run the following command to get the latest image. If you don't allow a non-root user to run Docker commands, you'll need to add sudo to the front of the command.
docker pull wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.5.1Upgrading Wallaroo Source Code
If you mounted the Wallaroo source code to your local machine using the directory recommended in setup, in /tmp/wallaroo-docker (UNIX & MacOS users) or c:/wallaroo-docker (Windows users), then you will need to move the existing directory in order to get the latest source code. The latest Wallaroo source code will be copied to this directory automatically when a new container is started with the latest Docker image.
UNIX & MacOS Users
For UNIX users, you can move the directory with the following command:
mv /tmp/wallaroo-docker/wallaroo-src/ /tmp/wallaroo-docker/wallaroo-0.5.0-src/Windows Users
For Windows users, you can move the directory with the following command:
move c:/wallaroo-docker/wallaroo-src/ c:/wallaroo-docker/wallaroo-0.5.0-srcOnce done moving, you can re-create the wallaroo-src directory with the following command:
mkdir c:\wallaroo-docker\wallaroo-srcUpgrading Wallaroo in Vagrant
To upgrade your Wallaroo installation in Vagrant, you’ll want to start by moving your current Vagrant directory to a new location. Assuming that you’ve installed it according to our setup documentation, you’ll run:
cd ~/
mv wallaroo-tutorial/wallaroo/vagrant wallaroo-tutorial/wallaroo/vagrant-0.5.0
cd ~/wallaroo-tutorial/wallaroo/
git fetch origin
git checkout -f 0.5.1Finally, to provision your new Vagrant box, run the following commands:
cd ~/wallaroo-tutorial/wallaroo/vagrant
vagrant upIf you have modified your old Vagrant VM in any way that you intend to persist, you’ll need to do that now. For example, copy any edited or new files from the old Vagrant VM to the new one. When you’ve completed that, it’s a good idea to clean up your old Vagrant box, by running:
cd ~/wallaroo-tutorial/wallaroo/vagrant-0.5.0
vagrant destroyCHANGELOG
[0.5.1] - 2018-08-01
Fixed
- Ensure that parallel stateless computations always run independently (PR #2322)
Changed
- Filter none/nil in Decoder for Python/Go API's (PR #2259)
0.5.0
Release Notes
We’ve been hard at work in Wallaroo Labs-ville with a whole slew of features & bug fixes just to help you succeed with Wallaroo! We’ve added support for dynamic keys -- now you aren’t required to pre-define your partition keys when you’re writing your app! Let us do the heavy lifting for you!
This release has breaking changes and some new additions! We’ve added a table of contents below to help you navigate to sections you might find relevant.
Table of Contents
Breaking Changes
Unfortunately, we had to break some things as part of adding dynamic keys. Below is a list of changes you might need to make as part of upgrading.
Python API
The to_state_partition_u64 method has been removed and should be replaced with to_state_partition. If you were using the to_state_partition_u64 method your partition_keys will also have to be updated from a list of non-negative integers to a list of strings.
Example
Our Python Market Spread example application, previously used to_state_partition_u64 in the ApplicationBuilder:
.to_state_partition_u64(
update_market_data, SymbolData, "symbol-data",
symbol_partition_function, symbol_partitions
)This method has been removed and is replaced with to_state_partition:
.to_state_partition(
update_market_data, SymbolData, "symbol-data",
symbol_partition_function, symbol_partitions
)Since the partition_keys were previously defined
using the str_to_partition method to convert the string data to non-negative integers, that too needed to be updated from this:
symbol_partitions = [str_to_partition(x.rjust(4)) for x in
load_valid_symbols()]To this, which is a list of strings:
symbol_partitions = [x.rjust(4) for x in
load_valid_symbols()]See the full application code here.
Go API
The ToStatePartition and ToStatePartitionMulti methods have been updated to no longer accept partition keys as a parameter. If you wish to continue using your partition keys, you’ll need to replace them with ToStatePartitionWithKeys and ToStatePartitionMultiWithKeys methods respectively.
The type of your partition keys will also need updating, you will need to change your partition methods to return a byte slice instead of a U64 slice.
Example
Our Go Alphabet example has been updated to replace ToStatePartition:
ToStatePartition(&AddVotes{}, &RunningVotesTotalBuilder{}, "running vote totals", &LetterPartitionFunction{}, MakeLetterPartitions())with ToStatePartitionWithKeys:
ToStatePartitionWithKeys(&AddVotes{}, &RunningVotesTotalBuilder{}, "running vote totals", &LetterPartitionFunction{}, MakeLetterPartitions())And the partition method was updated from returning a uint64 slice:
func (lpf *LetterPartitionFunction) Partition(data interface{}) []uint64 {
lav := data.(*LetterAndVotes)
return []uint64{lav.Letter}
}To a byte slice:
func (lpf *LetterPartitionFunction) Partition(data interface{}) []byte {
lav := data.(*LetterAndVotes)
return []byte{lav.Letter}
}See the full application code here.
New Additions
Here are some new features we've added to Wallaroo!
Dynamic Keys
You’re now able to let Wallaroo generate your partition keys as needed, rather than needing to declare all of them up front. We've covered in detail how to update your application to do in the Updating your application section.
Cluster Shrinker Tool
We’ve added a new tool to help operators shrink a Wallaroo cluster. It’s appropriately called the cluster_shrinker and instructions on its usage can be found in the Shrink to Fit section of our Autoscale documentation.
Support for Multiple Sinks per Pipeline
Pipelines now support multiple sinks! Check out the updated Python API docs and Go API docs for information on how to add multiple sinks to your Wallaroo application.
How to Upgrade your Application
So, will you be transitioning your application to use dynamic keys? Or will you continue to declare your partition keys in advance?
I like new things! Or, I need dynamic keys
Are you using the Go API, or the Python API?
Go
You’ll need to update some methods:
The ToStatePartition method no longer has a parameter for partition keys, so you’ll want to continue using this function. You’ll need to update all instances of it, though, to no longer pass in your partition keys. Similarly, ToStatePartitionMulti no longer has a parameter for partition keys, and you must ensure that you no longer pass any partition keys to it.
So for our Word Count example, the ToStatePartition method will change from this, without dynamic keys:
ToStatePartition(&CountWord{}, &WordTotalsBuilder{}, "word totals", &WordPartitionFunction{}, LetterPartition())To this, with dynamic keys:
ToStatePartition(&CountWord{}, &WordTotalsBuilder{}, "word totals", &WordPartitionFunction{})Python
In Python, the to_state_partition method has been updated to use an optional parameter for your partition keys. So, you don’t need to modify the method itself. However, since you want to use dynamic keys now, you’ll need to no longer send in your partition keys. To do this, you’ll have to update the methods’ arguments so that you no longer pass in your keys.
So for our Word Count examples, the to_state_partition method will change from this, without dynamic keys:
ab.to_state_partition(count_word, WordTotals, "word totals",
partition, word_partitions)To this, with dynamic keys:
ab.to_state_partition(count_word, WordTotal, "word totals",
partition)I know my keys in advance and want a performance bump
Are you using the Go API, or the Python API?
Go
You’ll need to update some methods:
ToStatePartition should be ToStatePartitionWithKeys and ToStatePartitionMulti should become ToStatePartitionMultiWithKeys.
So for our Word Count example, the ToStatePartition method will change from this, without dynamic keys:
ToStatePartition(&CountWord{}, &WordTotalsBuilder{}, "word totals", &WordPartitionFunction{}, LetterPartition())To this, with dynamic keys:
ToStatePartitionWithKeys(&CountWord{}, &WordTotalsBuilder{}, "word totals", &WordPartitionFunction{}, LetterPartition())Python
In Python, the to_state_partition method has been updated to use an optional parameter for your partition keys. So, you don’t need to modify the method itself. However, the keys now must be strings. Previously, keys could be any object, as long as it could be hashed, and checked for equality.
So if you previously partitioned on say, a list of integers:
partitions = [1,2,3,4,5,6,7,8,9,10]You’ll want to update the partitions to a list of strings instead, like so:
partitions = ["1","2","3","4","5","6","7","8","9","10"]And with those changes, you should be ready to get started with Wallaroo 0.5.0!
Upgrading Wallaroo
In all cases below, if you run into issues, please reach out to us! We’re available on twitter, IRC, Github, by email, or [our mailing list](https://group...
0.4.3
Release Notes
As part of the 0.4.3 release, we've added a precompiled version of Machida, our Wallaroo Python runner application, with Resilience turned on to the Wallaroo Docker image! Make sure you're setup with Docker and run:
docker pull wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.4.3
to get started with our latest image!
0.4.2
Release Notes
As part of the 0.4.2 release, we've added support for development in Vagrant. We've also added support for Windows with both Vagrant and Docker. Have a look at our "Choosing an Installation Option" documentation to get started!
Check out the rest of the release information below:
[0.4.2] - 2018-05-14
Fixed
- Improve Python exception handling in user provided functions (PR #2194)