What's coming in Airflow 2.0? - NYC Apache Airflow Meetup

What’s
coming in
Apache
Airflow 2.0
NYC Meetup
13th of May 2020

Who are we?
Tomek Urbaszek
Committer
Software Engineer @ Polidea
Jarek Potiuk
Committer, PMC member
Principal Software Engineer @ Polidea
Kamil Breguła
Committer
Software Engineer @ Polidea
Ash Berlin-Taylor
Airflow Engineering Lead @ Astronomer
Daniel Imberman
Committer
Senior Data Engineer @ Astronomer
Kaxil Naik
Senior Data Engineer @ Astronomer

Scheduler High Availability
Goals:
● Performance - reduce task-to-task schedule "lag"
● Scalability - increase task throughput by horizontal scaling
● Resiliency - kill a scheduler and have tasks continue to be scheduled

Scheduler High Availability: Design
● Active-active model. Each scheduler does everything
● Uses existing database - no new components needed, no extra operational
burden
● Plan to use row-level-locks in the DB
● Will re-evaluate if performance/stress testing show the need

Scheduler High Availability: Tasks
● Separate DAG parsing from DAG scheduling
This removes the tie between parsing and scheduling that is still present
● Run a mini scheduler in the worker after each task is completed
A.K.A. "fast follow". Look at immediate down stream tasks of what just finished and see what we can
schedule
● Test it to destruction
This is a big architectural change, we need to be sure it works well.

Dag Serialization (Tasks Completed)
● Stateless Webserver: Scheduler parses the DAG files, serializes them in JSON format & saves
them in the Metadata DB.
● Lazy Loading of DAGs: Instead of loading an entire DagBag when the Webserver starts we only
load each DAG on demand. This helps reduce Webserver startup time and memory. This
reduction is notable with large number of DAGs.
● Deploying new DAGs to Airflow - no longer requires long restarts of webserver (if DAGs are baked in
Docker image)
● Feature to use the “JSON” library of choice for Serialization (default is inbuilt ‘json’ library)
● Paves way for DAG Versioning & Scheduler HA

Dag Serialization (Tasks In-Progress for Airflow 2.0)
● Decouple DAG Parsing and Serializing from the scheduling loop.
● Scheduler will fetch DAGs from DB
● DAG will be parsed, serialized and saved to DB by a separate component
“Serializer”/ “Dag Parser”
● This should reduce the delay in Scheduling tasks when the number of DAGs
are large

Dag Versioning
Current Problem:
● Change in DAG structure affects viewing previous DagRuns too
● Not possible to view the code associated with a specific DagRun

Dag Versioning (Current Problem)

Dag Versioning (Current Problem)
New task is shown in Graph View for older DAG Runs too with “no status”.

Dag Versioning
Current Problem:
● Change in DAG structure affects viewing previous DagRuns too
● Not possible to view the code associated with a specific DagRun
Goal:
● Support for storing multiple versions of Serialized DAGs
● Baked-In Maintenance DAGs to cleanup old DagRuns & associated
Serialized DAGs
● Graph View shows the DAG associated with that DagRun

Performance improvements
● Review each component of scheduler in turn and its optimization.
● Perf kit
○ A set of tools that allows you to quickly check the performance of a component

Do you see a performance problem?

Results for DagFileProcessor
When we have one DAG file with 200 DAGs, each DAG with 5 tasks:
Before After Diff
Average time: 8080.246 ms 628.801 ms -7452 ms (92%)
Queries count: 2692 5 -2687 (99%)

API: follows Open API 3.0 specification
Outreachy interns
Ephraim Anierobi
Omair Khan

CI environment
● Moving to GitHub Actions
○ Kubernetes Tests
○ Easier way to test Kubernetes Tests locally
● Quarantined tests
○ Process of fixing the Quarantined tests
● Thinning CI image
○ Move integrations out of the image (hadoop etc)
● Automated System Tests (AIP-21)

Dev environment
● Breeze
○ unit testing
○ package building
○ release preparation
○ refreshing videos
● CodeSpaces integration

Backport Packages
● Bring Airflow 2.0 providers to 1.10.*
● Packages per-provider
● 58 packages (!)
● Python 3.6+ only(!)
● Automatically tested on CI
● Future
○ Automated System Tests (AIP-21)
○ Split Airflow (AIP-8)?

Automated release notes for backport packages

Support for Production
Deployments

Production Image
● Alpha quality image is ready
● Gathering feedback
● Started with “bare image”
● Listening to use cases from users
● Integration with Docker Compose
● Integration with Helm Chart

KubernetesExecutor vs. CeleryExecutor

KEDA Autoscaling
● Kubernetes Event-driven Autoscaler
● Scales based on # of RUNNING and QUEUED tasks in PostgreSQL backend

KEDA Queues
● Historically Queues were expensive and hard to allocate
● With KEDA, queues are free! (can have 100 queues)
● KEDA works with k8s deployments so any customization you can make in a
k8s pod, you can make in a k8s queue (worker size, GPU, secrets, etc.)

KubernetesExecutor
Pod Templating from
YAML/JSON

KubernetesExecutor Pod Templating
● In the K8sExecutor currently, users can modify certain parts of the pod, but
many features of the k8s API are abstracted away
● We did this because at the time the airflow community was not well
acquainted with the k8s API
● We want to enable users to modify their worker pods to better match their
use-cases

KubernetesExecutor Pod Templating
● Users can now set the pod_template_file config in their
airflow.cfg
● Given a path, the KubernetesExecutor will now parse the yaml
file when launching a worker pod
● Huge thank you to @davlum for this feature

Helm Chart
● Donated by astronomer.io.
● This is the official helm chart that we have used both in our
enterprise and in our cloud offerings (thousands of deployments
of varying sizes)
● Users can turn on KEDA autoscaling through helm variables

Helm Chart
● Chart will cut new releases with each airflow release
● Will be tested on official docker image
● Significantly simplifies airflow onboarding process for
Kubernetes users

Functional DAGs
➔ PythonOperator boilerplate code
➔ Define order and data relation
separately
➔ Writing jinja strings by hand

Functional DAGs
No PythonOperator boilerplate code!
Data and order relationship are same!
And works for all operators

Functional DAGs
AIP-31: Airflow functional DAG definition
➔ Easy way to convert a function to
an operator
➔ Simplified way of writing DAGs
➔ Pluggable XCom Storage engine
Example: store and retrieve DataFrames on
GCS or S3 buckets without boilerplate code

Other changes of note
● Connection IDs now need to be unique
It was often confusing, and there are better ways to do load balancing
● Python 3 only
Python 2.7 unsupported upstream since Jan 1, 2020
● "RBAC" UI is now the only UI.
Was a config option before, now only option. Charts/data profiling removed due to security risks

When will Airflow 2.0 be available?

Airflow 2.0 – deprecate, but (try) not to remove
● Breaking changes should be avoided where we can – if upgrade is to difficult
users will be left behind
● Release "backport providers" to make new code layout available "now":
● Before 2.0 we want to make sure we've fixed everything we want to remove
or break.
pip install apache-airflow-backport-providers-aws
apache-airflow-backport-providers-google

How to upgrade to 2.0 safely
● Install the latest 1.10 release
● Run airflow upgrade-check (doesn't exist yet)
● Fix any warnings
● Upgrade Airflow

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