Conductor is a workflow orchestration engine developed by Netflix, widely used for various content engineering processes. Recent updates include the introduction of a gRPC framework, enhanced workflow management features, and the ability to dynamically create workflows. The Netflix community continues to contribute to Conductor's enhancement, with an ongoing wish list for future features aimed at improving functionality and user experience.

1. Evolution of Netflix Conductor

2. Conductor is a workflow orchestration engine developed and open-sourced by
Netflix. If you’re new to Conductor, this earlier blog post and the
documentation should help you get started and acclimatized to Conductor.
Over the most recent a long time since commencement, Conductor has seen
wide appropriation and is instrumental in running various center work
processes at Netflix. A considerable lot of the Netflix Content and Studio
Engineering administrations depend on Conductor for proficient handling of
their business streams. The Netflix Media Database (NMDB) is one such
model.
In this blog, we might want to show the most recent updates to Conductor,
address a portion of the much of the time posed inquiries and thank the
network for their commitments.

3. Visit here for more details office setup

4. How we’re using Conductor at Netflix
1.Deployment
Conductor is one of the most heavily used services within Content Engineering at Netflix.
Of the multitude of modules that can be plugged into Conductor as shown in the image
below, we use the Jersey server module, Cassandra for persisting execution data,Dynomite
for persisting metadata, Dyno Queues as the queuing recipe built on top of Dynomite,
Elastic search as the secondary data store and indexer, and Netflix Spectator + Atlas for
Metrics. Our cluster size ranges from 12–18 instances of AWS EC2 m4.4xlarge instances,
typically running at ~30% capacity.
We do not maintain an internal fork of Conductor within Netflix. Instead, we use a
wrapper that pulls in the latest version of Conductor and adds Netflix infrastructure
components and libraries before deployment. This allows us to proactively push changes to
the open source version while ensuring that the changes are fully functional and well-
tested.
2. Adoption
As of writing this blog, Conductor orchestrates 600+ workflow definitions owned by 50+
teams across Netflix. While we’re not (yet) actively measuring the nth percentiles, our
production workloads speak for Conductor’s performance. Below is a snapshot of our
Kibana dashboard which shows the workflow execution metrics over a typical 7-day period.

5. Use Cases
Some of the use cases served by Conductor at Netflix can be categorized under:
• Content Ingest and Delivery
• Content Quality Control
• Content Localization
• Encodes and Deployments
• IMF Deliveries
• Marketing Tech
• Studio Engineering
What’s New
gRPC Framework
One of the key features in v2.0 was the introduction of the gRPC
framework as an alternative/auxiliary to REST. This was contributed by
our counterparts at GitHub, thereby strengthening the value of
community contributions to Conductor.

6. Cassandra Persistence Layer
To enable horizontal scaling of the data store for large volume of concurrent
workflow executions (millions of workflows/day), Cassandra was chosen to provide
elastic scaling and meet throughput demands.
External Payload Storage
External payload storage was implemented to prevent the usage of Conductor as a
data persistence system and to reduce the pressure on its backend data store.
Dynamic Workflow Executions
For use cases where the need arises to execute a large/arbitrary number of varying
workflow definitions or to run a one-time ad hoc workflow for testing or analytical
purposes, registering definitions first with the metadata store in order to then
execute them only once, adds a lot of additional overhead. The ability to
dynamically create and execute workflows removes this friction. This was another
great addition that stemmed from our collaboration with GitHub.

7. Workflow Status Listener
Conductor can be configured to publish notifications to external systems or queues
upon completion/termination of workflows. The workflow status listener provides
hooks to connect to any notification system of your choice. The community has
contributed an implementation that publishes a message on a dyno queue based on the
status of the workflow. An event handler can be configured on these queues to trigger
workflows or tasks to perform specific actions upon the terminal state of the workflow.
Bulk Workflow Management
There has always been a need for bulk operations at the workflow level from an
operability standpoint. When running at scale, it becomes essential to perform
workflow level operations in bulk due to bad downstream dependencies in the worker
processes causing task failures or bad task executions. Bulk APIs enable the operators
to have macro-level control on the workflows executing within the system.
Decoupling Elastic search from Persistence
This inter-dependency was removed by moving the indexing layer into separate
persistence modules, exposing a property (workflow.elasticsearch.instanceType) to
choose the type of indexing engine. Further, the indexer and persistence layer have
been decoupled by moving this orchestration from within the primary persistence layer
to a service layer through the Execution DAOFacade.

8. ES5/6 Support
Support for Elastic search versions 5 and 6 have been added as part of the major
version upgrade to v2.x. This addition also provides the option to use the Elastic
search Rest Client instead of the Transport Client which was enforced in the
previous version. This opens the route to using a managed Elastic search cluster (a
la AWS) as part of the Conductor deployment.
Task Rate Limiting & Concurrent Execution Limits
Task rate limiting helps achieve bounded scheduling of tasks. The task definition
parameter rateLimitFrequencyInSeconds sets the duration window,
while rateLimitPerFrequency defines the number of tasks that can be scheduled
in a duration window. On the other hand, concurrentExecLimitprovides unbounded
scheduling limits of tasks. I.e the total of current scheduled tasks at any given
time will be under concurrentExecLimit. The above parameters can be used in
tandem to achieve desired throttling and rate limiting.

9. API Validations
Validation was one of the core features missing in Conductor 1.x. To improve
usability and operability, we added validations, which in practice has greatly helped
find bugs during creation of workflow and task definitions. Validations enforce the
user to create and register their task definitions before registering the workflow
definitions using these tasks. It also ensures that the workflow definition is well-
formed with correct wiring of inputs and outputs in the various tasks within the
workflow. Any anomalies found are reported to the user with a detailed error
message describing the reason for failure.
Developer Labs, Logging and Metrics
We have been continually improving logging and metrics, and revamped the
documentation to reflect the latest state of Conductor. To provide a smooth on
boarding experience, we have created developer labs, which guides the user
through creating task and workflow definitions, managing a workflow lifecycle,
configuring advanced workflows with eventing etc., and a brief introduction to
Conductor API, UI and other modules.

11. New Task Types
System tasks have proven to be very valuable in defining the Workflow structure and
control flow. As such, Conductor 2.x has seen several new additions to System tasks,
mostly contributed by the community:
Lambda
Lambda Task executes ad-hoc logic at Workflow run-time, using the Nashorn Javascript
evaluator engine. Instead of creating workers for simple evaluations, Lambda task enables
the user to do this inline using simple Javascript expressions.
Terminate
Terminate task is useful when workflow logic should terminate with a given output. For
example, if a decision task evaluates to false, and we do not want to execute remaining
tasks in the workflow, instead of having a DECISION task with a list of tasks in one case
and an empty list in the other, this can scope the decide and terminate workflow
execution.

12. ExclusiveJoin
Exclusive Join task helps capture task output from a DECISION task’s flow. This is useful
to wire task inputs from the outputs of one of the cases within a decision flow. This
data will only be available during workflow execution time and the ExclusiveJoin task
can be used to collect the output from one of the tasks in any of decision branches.
For in-depth implementation details of the new additions, please refer
the documentation.
What’s next
There are a lot of features and enhancements we would like to add to Conductor. The
below wish list could be considered as a long-term road map. It is by no means exhaustive,
and we are very much welcome to ideas and contributions from the community. Some of
these listed in no particular order are:
Advanced Eventing with Event Aggregation and Distribution
At the moment, event generation and processing is a very simple implementation. An event
task can create only one message, and a task can wait for only one event.

13. We envision an Event Aggregation and Distribution mechanism that would open up
Conductor to a multitude of use-cases. A coarse idea is to allow a task to wait for
multiple events, and to progress several tasks based on one event.
UI Improvements
While the current UI provides a neat way to visualize and track workflow executions,
we would like to enhance this with features like:
• Creating metadata objects from UI
• Support for starting workflows
• Visualize execution metrics
• Admin dashboard to show outliers
New Task types like Goto, Loop etc.
Conductor has been using a Directed Acyclic Graph (DAG) structure to define a
workflow. The Goto and Loop on tasks are valid use cases, which would deviate from
the DAG structure. We would like to add support for these tasks without violating the
existing workflow execution rules. This would help unlock several other use cases
like streaming flow of data to tasks and others that require repeated execution of a
set of tasks within a workflow.

14. Support for reusable commonly used tasks like Email, DatabaseQuery etc.
Similarly, we’ve seen the value of shared reusable tasks that does a specific thing.
At Netflix internal deployment of Conductor, we’ve added tasks specific to
services that users can leverage over recreating the tasks from scratch. For
example, we provide a TitusTask which enables our users to launch a new Titus
container as part of their workflow execution.
We would like to extend this idea such that Conductor can offer a repository of
commonly used tasks.
Push based task scheduling interface
Current Conductor architecture is based on polling from a worker to get tasks that
it will execute. We need to enhance the grpc modules to leverage the
bidirectional channel to push tasks to workers as and when they are scheduled,
thus reducing network traffic, load on the server and redundant client calls.

15. Validating Task inputKeys and outputKeys
This is to provide type safety for tasks and define a parameterized interface for task
definitions such that tasks are completely re-usable within Conductor once
registered. This provides a contract allowing the user to browse through available
task definitions to use as part of their workflow where the tasks could have been
implemented by another team/user. This feature would also involve enhancing the UI
to display this contract.
Implementing MetadataDAO in Cassandra
As mentioned here, Cassandra module provides a partial implementation for
persisting only the workflow executions. Metadata persistence implementation is not
available yet and is something we are looking to add soon.
Pluggable Notifications on Task completion
Similar to the Workflow status listener, we would like to provide extensible interfaces
for notifications on task execution.

16. Python client in Pypi
We have seen wide adoption of Python client within the community. However,
there is no official Python client in Pypi, and lacks some of the newer additions
to the Java client. We would like to achieve feature parity and publish a client
from Conductor Github repository, and automate the client release to Pypi.
Removing Elasticsearch from critical path
While Elasticsearch is greatly useful in Conductor, we would like to make this
optional for users who do not have Elasticsearch set-up. This means removing
Elasticsearch from the critical execution path of a workflow and using it as an
opt-in layer.
Pluggable authentication and authorization
Conductor doesn’t support authentication and authorization for API or UI, and
is something that we feel would add great value and is a frequent request in
the community.

17. Validations and Testing
Dry runs, i.e the ability to evaluate workflow definitions without actually running it
through worker processes and all relevant set-up would make it much easier to test
and debug execution paths.
If you would like to be a part of the Conductor community and contribute to one of the
Wishlist items or something that you think would provide a great value add, please read
through this guide for instructions or feel free to start a conversation on our Gitter
channel, which is Conductor’s user forum.
We also highly encourage to polish, genericize and share any customizations that you may
have built on top of Conductor with the community.
We really appreciate and are extremely proud of the community involvement, who have
made several important contributions to Conductor. We would like to take this further and
make Conductor widely adopted with a strong community backing.
Visit here for more details office setup today