Virtual Flink Forward 2020: Autoscaling Flink at Netflix - Timothy Farkas

Autoscaling
Timothy Farkas
Senior Software Engineer @ Netflix

Our Pain
● Thousands of stateless single source and single sink Flink routers.
● All operators are chained.
● When lag for a router exceeds a threshold we are paged.
Kafka
Consumer
Project
Filter
Sink

Definitions
● Workload: Events being produced to a kafka topic. Two main knobs to turn:
○ Message Rate
○ Message Size
● Lag: The time it would take for a router to process all the remaining
unprocessed events that are buffered in its kafka topic.
● Healthy Router: A router is healthy if it’s lag is always under ~5 minutes.
● Autoscaling Solution: Adjust the number of nodes in the router dynamically
based on the workload to keep the router healthy. Attempt to use the
smallest number of nodes that are required to keep the pipeline healthy.

Solution Space
● Claim: There is no perfect solution. Any autoscaling algorithm can be defeated
by one or more workloads.
● Proof: Take any autoscaling algorithm A. Provide A with a workload W that
does the exact opposite of what A expects whenever A decides to resize the
cluster. =>
A will always make the wrong decision for W by definition. =>
Q.E.D.
○ Understand our limitations.
○ Make assumptions about our workloads.
○ Make a solution that works well when taking both into account.

Limitations
● Rescaling introduces processing pauses.
● Scaling down a Flink job suspends processing for 1 - 3 minutes and possibly
more.
○ CHEAP: Graceful shutdown with savepoint.
○ EXPENSIVE: Remove TMs.
○ CHEAP: Restart from savepoint with reduced parallelism.
● Scaling up a Flink job suspends processing for period < 1 minute.
○ EXPENSIVE: Add TMs.
○ CHEAP: Restart from savepoint with increased parallelism.
● There is a two minute delay for propagating metrics through Netflix’s metrics
infrastructure.

Assumptions
● Better to accidentally over allocate than to under allocate.
● Average message size changes infrequently.
● Large spikes in the workload happen, but not frequently.
● Workloads tend to smoothly increase or decrease, when they don’t have a
large spike.

Desirable Characteristics
● Minimal amount of state
● Deterministic behavior
● Easy to unit test
● Easy to control

Approaches
● Historical Prediction
● Rule Based
● PID Controller
● Statistical Short Term Prediction + Policies

Autoscaling - High Level Steps
● Collect: Receive a batch of metrics for the current 1 minute timebucket.
● Pre-Decision Policy: Apply policies which decide whether the cluster can be
rescaled or whether performance information can be collected about the cluster.
● Decide: Based on latest batch of events, decide whether to:
○ Scale up
○ Scale down
○ Stay the same
○ Also collects cluster performance information
● Calculate Size: If scaling up or down, decide how many nodes need to be
added or removed.
● Post-Decision Policy: Apply policies which can modify scale up and scale
down decisions.

Metrics Collection
Each minute collect the following
● Kafka consumer lag
● Records processed per second
● Cpu utilization
● Max Message Latency
Kafka
Kafka
Consumer
Buffered Events
Router
Sink
Processing
Filter /
Projection
● Kafka messages in per second
● Net in / out utilization
● Sink health metrics
Store the metrics for the past N minutes to inform scaling decisions and to do
regression to predict the workload.

Pre-Decision Policy
Abort autoscaling process if:
● The router has recent task failures
● The router is currently redeploying

Decide - Scale Up
Scale up if:
● There is significant lag AND sink is healthy
● Utilization exceeds the safe threshold AND sink is healthy
Key Insight - Collect cluster performance information:
● If the cluster needs to be scaled up that means the cluster is saturated.
● This is effectively a benchmark for the performance of the cluster at the current
size.
● Save this information in a Performance Table for future scaling decisions.
● More on this in the Performance Table section later.

Decide - Scale Down
Scale down if:
● There is no lag AND we do not anticipate an increase in incoming messages
● More on how we anticipate incoming message rate in the Predict Workload
section later.

Calculate Size
● Predict Workload: Predict the future workload (messages in per second),
while taking spikes into account.
● Target Events Per Second: Compute target events / sec that the pipeline will
need to handle X minutes from now.
● Cluster Size Lookup: Use the target events / sec to estimate the desired
cluster size, which can handle the workload up to X minutes from now.

● Quadratic regression for troughs: ax^2 + bx + c
● Linear regression for everything else: ax + b
Predict Workload

● Assume error for regression is normally distributed and centered at 0.
● Find standard deviation of error.
● Any error greater than 3 * sigma is an outlier.
● After enough consecutive outliers are observed, the baseline is reset.
Spike Detection

Var = (1 + 1 + 1 + 1) / 6 std = sqrt(4 / 6) 3 *std = 2.45
Spike Detection - First Outlier

Spike Detection - Baseline Reset

restartTime catchUpTime
recoveryTime = 15 min
2 minutes 13 minutes
t1 t2 = t1 + 15 min
Compute Target Processing Rate
t3 = t1 + 20 min
validTime
1.
2.
3.

● Lag and resource usage is high =>
● Pipeline is saturated =>
● We decide to scale up =>
● We know the maximum throughput of the current cluster at the current size =>
● Record the performance in a lookup table
Cluster Size Lookup - The Performance Table

● Given a target rate find the performance records above and below it.
● Do linear interpolation to find the suitable cluster size.
Num Nodes Max Rate
4 10,000
10 20,000
18 35,000
targetRate = 15,000
ratio = (15000 - 10000)
(20000 - 10000)
ratio = .5
clusterSize = .5 (4) + .5 (10)
clusterSize = 7
Performance Table
Cluster Size Lookup - The Performance Table

Num Nodes Max Rate
4 10,000
10 20,000
18 35,000
targetRate = 40,000
clusterSize = (40,000) = 20.57
(35,000 / 18)
ceil(clusterSize) = 21
Performance Table
Cluster Size Lookup - Corner Case

Cluster Size Lookup - Complexities
● Few more corner cases
● Utilization also needs to be taken into account
● Want new cluster size to have reasonable resource utilization 60% or less

Calculate Size
Scale Up vs Scale Down
● Flow and logic is the same
● Minor differences in implementation details

Post-Decision Policy
● Minimum cluster size based on partition count of Kafka topic
● Maximum cluster size based on partition count of Kafka topic
● Cooldown period for scale ups
● Cooldown period for scale downs
● Disable scale downs during region failover (see Region Failover section)
● Safety limit for max scale up. Ex. cannot add more than 50 nodes during a
scale up
● Safety limit for max cluster size

Architecture Options
● Embed autoscaling in Flink
○ Pros:
■ Lower latency for retrieving metrics.
○ Cons:
■ Complex resource manager interactions get pushed down into Flink.
■ Rescale operation not easily integrated into operations history for the job.
■ Autoscaling changes requires redeploy of the job.
● Run autoscaling as a Mantis pipeline
○ Pros:
■ Flink service control plane handles all resource manager interactions already and it can be
re-used for rescaling the job.
■ Flink service control plane keeps history of all rescale actions.
■ Autoscaling can be changed without redeploying jobs.
○ Cons:
■ 2 minute latency for getting metrics.

Titus
Autoscaling Architecture
Autoscaler
(Mantis Job)
User
Router
User
Router
User
Router
Spinnaker
Flink as a Service
Atlas

Memory Requirements
● Direct memory has to be preserved for Kafka Consumer and Kafka
Producer
● Direct memory cannot be changed for TMs that are already running
● Smaller clusters require more Direct memory (each node handles more
partitions)
● Larger clusters require less Direct memory (each node handles fewer
partitions)
● Deploy cluster with Direct memory that works for the minimum cluster
size

Partition Balancing
2 Partitions
2 Partitions
1 Partition
1 Partition
1 Partition
1 Partition
● 3 TMs
● 2 Task Slots per TM
● Topic with 8 partitions
TMs with N Task Slots in the worst case
can be assigned N more partitions than
other TMs
● Aggregate consumer lag and
average message latency may
be low.
● A few partitions may have high
latency due to unbalanced
distribution of partitions.
● Round up the cluster so that the
maximum possible partitions per
subtask is reduced by 1.
● Note this is a much looser
requirement on cluster size than
requiring equal distribution of
partitions to subtasks. This
allows finer grained cluster size
control.

Outlier Containers
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: HANDLING MCE MEMORY ERROR
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: CPU 24: Machine Check Event: 0 Bank 13:
cc063f80000800c0
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: TSC 0
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: ADDR 57c24fb4c0
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: MISC 908511010100086
[Wed Sep 4 17:50:03 2019] EDAC skx MC2: PROCESSOR 0:50654 TIME 1567619410 SOCKET 1 APIC 40
[Wed Sep 4 17:50:03 2019] EDAC MC2: 6398 CE memory scrubbing error on
CPU_SrcID#1_MC#0_Chan#0_DIMM#0 (channel:0 slot:0 page:0x57c24fb offset:0x4c0 grain:32
syndrome:0x0 - OVERFLOW err_code:0008:00c0 socket:1 imc:0 rank:1 bg:3 ba:2 row:1aacf col:338)

Region Failover
Netflix
us-west
Netflix
us-east
Netflix
eu-west
User
us-west
User
us-east
User
eu-west
Flink Routers
us-west
Flink Routers
us-west
Flink Routers
us-west
Disable scaledowns in the evacuated
region until traffic comes back.

Eager Scale Up
● Current Scale Up Decision:
○ There is significant consumer lag AND sink is healthy
○ Utilization exceeds the safe threshold AND sink is healthy
This is not ideal since in most cases latency builds up in the job before a scale up is
triggered.
● Eager Scale Up Decision:
○ Use the performance table to determine the maximum processing rate of the
current cluster.
○ Use regression to determine if the workload will exceed the processing rate
of the cluster in the near future.
○ If this is the case do a scale up before any lag builds up.

Downscale Optimization
● Current downscale operation
● Optimized downscale operation
○ CHEAP: Blacklist TMs that will be removed.

Complex DAGs
● Extend the algorithm to support multiple sources and sinks.
● Handle jobs where all operators are not chained.

Acknowledgements
● Steven Wu
● Andrew Nguonly
● Neeraj Joshi
● Mark Cho
● Netflix Flink Team
● Netflix Mantis Team
● Netflix Data Pipeline Team
● Netflix RTDI Team
● https://www.spinnaker.io/
● https://github.com/Netflix/mantis

Virtual Flink Forward 2020: Autoscaling Flink at Netflix - Timothy Farkas

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Virtual Flink Forward 2020: Autoscaling Flink at Netflix - Timothy Farkas