Performance and Fault Tolerance for the Netflix API

Ben ChristensenSoftware Engineer – API Platform at Netflix@benjchristensenhttp://www.linkedin.com/in/benjchristensen

http://techblog.netflix.com/

Silicon Valley Cloud Computing Group - July 18 2012

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Netflix API

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2The Netflix API serves all streaming devices and acts as the broker between backend Netflix systems and the user interfaces running on the 800+ devices that support Netflix streaming.

More than 1 billion incoming calls per day are received which in turn fans out to several billion outgoing calls (averaging a ratio of 1:6) to dozens of underlying subsystems with peaks of over 200k dependency requests per second.

Netflix API

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3First half of the presentation discusses resilience engineering implemented to handle failure and latency at the integration points with the various dependencies.

Dozens of dependencies.

One going bad takes everything down.

99.99%30 = 99.7% uptime0.3% of 1 billion = 3,000,000 failures

2+ hours downtime/montheven if all dependencies have excellent uptime.

Reality is generally worse.

4Even when all dependencies are performing well the aggregate impact of even 0.01% downtime on each of dozens of services equates to potentially hours a month of downtime if not engineered for resilience.

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7Latency is far worse for system resilience than failure. Failures naturally “fail fast” and shed load whereas latency backs up queues, threads and system resources and if isolation techniques are not used it can cause an entire system to fail.

"Timeout guard" daemon prio=10 tid=0x00002aaacd5e5000 nid=0x3aac runnable [0x00002aaac388f000] java.lang.Thread.State: RUNNABLEat java.net.PlainSocketImpl.socketConnect(Native Method)at java.net.AbstractPlainSocketImpl.doConnect(AbstractPlainSocketImpl.java:339)- locked <0x000000055c7e8bd8> (a java.net.SocksSocketImpl)at java.net.AbstractPlainSocketImpl.connectToAddress(AbstractPlainSocketImpl.java:200)at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:182)at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:391)at java.net.Socket.connect(Socket.java:579)at java.net.Socket.connect(Socket.java:528)at java.net.Socket.(Socket.java:425)at java.net.Socket.(Socket.java:280)at org.apache.commons.httpclient.protocol.DefaultProtocolSocketFactory.createSocket(DefaultProtocolSocketFactory.java:80)at org.apache.commons.httpclient.protocol.ControllerThreadSocketFactory$1.doit(ControllerThreadSocketFactory.java:91)at org.apache.commons.httpclient.protocol.ControllerThreadSocketFactory$SocketTask.run(ControllerThreadSocketFactory.java:158) at java.lang.Thread.run(Thread.java:722)

[Sat Jun 30 04:01:37 2012] [error] proxy: HTTP: disabled connection for (127.0.0.1)

> 80% of requests rejected

Median Latency

8This is an example of what a system looks like when high latency occurs without load shedding and isolation. Backend latency spiked (from <100ms to >1000ms at the median, >10,000 at the 90th percentile) and saturated all available resources resulting in the HTTP layer rejecting over 80% of requests.

No single dependency should take down the entire app.

Fallback.Fail silent.Fail fast.

Shed load.

9It is a requirement of high volume, high availability applications to build fault and latency tolerance into their architecture. Infrastructure is an aspect of resilience engineering but it can not be relied upon by itself - software must be resilient.

10Netflix uses a combination of aggressive network timeouts, tryable semaphores and thread pools to isolate dependencies and limit impact of both failure and latency.

Tryable semaphores for “trusted” clients and fallbacks

Separate threads for “untrusted” clients

Aggressive timeouts on threads and network callsto “give up and move on”

Circuit breakers as the “release valve”

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12With isolation techniques the application container is now segmented according to how it uses its underlying dependencies instead of using a single shared resource pool to communicate with all of them.

13A single dependency failing will no longer be permitted to take more resources than it was allocated and can have its impact controlled.

14In this case the backend service has become latent and saturates all available threads allocated to it so further requests to it are rejected (the orange line) instead of blocking or using up all available system threads.

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30 rps x 0.2 seconds = 6 + breathing room = 10 threads

Thread-pool Queue size: 5-10 (0 doesn't work but get close to it)

Thread-pool Size + Queue Size

Queuing is Not Free

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Cost of Thread @ ~60rpsmean - median - 90th - 99th (time in ms)

Time for thread to execute Time user thread waited

17The Netflix API has ~30 thread pools with 5-20 threads in each. A common question and concern is what impact this has on performance.

Here is a sample of a dependency circuit for 24 hours from the Netflix API production cluster with a rate of 60rps per server.

Each execution occurs in a separate thread with mean, median, 90th and 99th percentile latencies shown in the first 4 legend values. The second group of 4 values is the user thread waiting on the dependency thread and shows the total time including queuing, scheduling, execution and waiting for the return value from the Future.

This example was chosen since it is relatively high volume and low latency so the cost of a separate thread is potentially more of a concern than if the backend network latency was 100ms or higher.

Cost of Thread @ ~60rpsmean - median - 90th - 99th (time in ms)

Time for thread to execute Time user thread waitedCost: 0ms18

At the median (and lower) there is no cost to having a separate thread.

Cost of Thread @ ~60rpsmean - median - 90th - 99th (time in ms)

Time for thread to execute Time user thread waitedCost: 3ms19

At the 90th percentile there is a cost of 3ms for having a separate thread.

Cost of Thread @ ~60rpsmean - median - 90th - 99th (time in ms)

Time for thread to execute Time user thread waitedCost: 9ms20

At the 99th percentile there is a cost of 9ms for having a separate thread. Note however that the increase in cost is far smaller than the increase in execution time of the separate thread which jumped from 2 to 28 whereas the cost jumped from 0 to 9.

This overhead at the 90th percentile and higher for circuits such as these has been deemed acceptable for the benefits of resilience achieved.

For circuits that wrap very low latency requests (such as those primarily hitting in-memory caches) the overhead can be too high and in those cases we choose to use tryable semaphores which do not allow for timeouts but provide most of the resilience benefits without the overhead. The overhead in general is small enough that we prefer the isolation benefits of a separate thread.

Cost of Thread @ ~75rpsmean - median - 90th - 99th (time in ms)

Time user thread waitedTime for thread to execute

21This is a second sample of a dependency circuit for 24 hours from the Netflix API production cluster with a rate of 75rps per server.

As with the first example this was chosen since it is relatively high volume and low latency so the cost of a separate thread is potentially more of a concern than if the backend network latency was 100ms or higher.

Each execution occurs in a separate thread with mean, median, 90th and 99th percentile latencies shown in the first 4 legend values. The second group of 4 values is the user thread waiting on the dependency thread and shows the total time including queuing, scheduling, execution and waiting for the return value from the Future.

Cost of Thread @ ~75rpsmean - median - 90th - 99th (time in ms)

Time user thread waitedTime for thread to executeCost: 0ms22

At the median (and lower) there is no cost to having a separate thread.

Cost of Thread @ ~75rpsmean - median - 90th - 99th (time in ms)

Time user thread waitedTime for thread to executeCost: 2ms23

At the 90th percentile there is a cost of 2ms for having a separate thread.

Cost of Thread @ ~75rpsmean - median - 90th - 99th (time in ms)

Time user thread waitedTime for thread to executeCost: 2ms24

At the 99th percentile there is a cost of 2ms for having a separate thread.

Netflix DependencyCommand Implementation

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Netflix DependencyCommand Implementation(1) Construct DependencyCommand Object

On each dependency invocation its DependencyCommand object will be constructed with the arguments necessary to make the call to the server.

For example:

DependencyCommand command = new DependencyCommand(arg1, arg2)

(2) Execution Synchronously or Asynchronously

Execution of the command can then be performed synchronously or asychronously:

K value = command.execute()

Future<K> value = command.queue()

The synchronous call execute() invokes queue().get() unless the command is specified to not run in a thread.

(3) Is Circuit Open?

Upon execution of the command it first checks with the circuit-breaker to ask "is the circuit open?".

If the circuit is open (tripped) then the command will not be executed and flow routed to (8) DependencyCommand.getFallback().

If the circuit is closed then the command will be executed and flow continue to (5) DependencyCommand.run().

(4) Is Thread Pool/Queue Full?

If the thread-pool and queue associated with the command is full then the execution will be rejected and immediately routed through fallback (8).

If the command does not run within a thread then this logic will be skipped.

(5) DependencyCommand.run()

The concrete implementation run() method is executed.

(5a) Command Timeout

The run() method occurs within a thread with a timeout and if it takes too long the thread will throw a TimeoutException. In that case the response is routed through fallback (8) and the eventual run() method response is discarded.

If the command does not run within a thread then this logic will not be applicable.

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Netflix DependencyCommand Implementation(6) Is Command Successful?

Application flow is routed based on the response from the run() method.

(6a) Successful Response

If no exceptions are thrown and a response is returned (including a null value) then it proceeds to return the response after some logging and a performance check.

(6b) Failed Response

When a response throws an exception it will mark it as "failed" which will contribute to potentially tripping the circuit open and it will route application flow to (8) DependencyCommand.getFallback().

(7) Calculate Circuit Health

Successes, failures, rejections and timeouts are all reported to the circuit breaker to maintain a rolling set of counters which calculate statistics.

These stats are then used to determine when the circuit should "trip" and become open at which point subsequent requests are short-circuited until a period of time passes and requests are permitted again after health checks succeed.

(8) DependencyCommand.getFallback()

The fallback is performed whenever a command execution fails (an exception is thrown by (5) DependencyCommand.run()) or when it is (3) short-circuited because the circuit is open.

The intent of the fallback is to provide a generic response without any network dependency from an in-memory cache or other static logic.

(8a) Fallback Not Implemented

If DependencyCommand.getFallback() is not implemented then an exception with be thrown and the caller left to deal with it.

(8b) Fallback Successful

If the fallback returns a response then it will be returned to the caller.

(8c) Fallback Failed

If DependencyCommand.getFallback() fails and throws an exception then the caller is left to deal with it.

This is considered a poor practice to have a fallback implementation that can fail. A fallback should be implemented such that it is not performing any logic that would fail. Semaphores are wrapped around fallback execution to protect against software bugs that do not comply with this principle, particular if the fallback itself tries to perform a network call that can be latent.

(9) Return Successful Response

If (6a) occurred the successful response will be returned to the caller regardless of whether it was latent or not.

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Netflix DependencyCommand Implementation

Fallbacks

CacheEventual Consistency

Stubbed DataEmpty Response

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Netflix DependencyCommand Implementation

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So, how does it work in the real world?

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Visualizing Circuits in Near-Realtime(latency is single-digit seconds, generally 1-2)

31This is an example of our monitoring system which provides low-latency (1-2 seconds typically) visibility into the traffic and health of all DependencyCommand circuits across a cluster.

Rolling 10 second counters with 1 second granularity

last minute latency percentiles

2 minutes of request rate to show relative changes in traffic

circle color and size represent health and traffic volume

hosts reporting from cluster

Error percentage of last 10 second

Request rate

Failures/ExceptionsThread-pool RejectionsThread timeoutsSuccesses

Short-circuited (rejected)

Circuit-breaker status

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Weekend Weekend Weekend

8-10 Billion DependencyCommand Executions (threaded)

1.2 - 1.6 Billion Incoming Requests

API Daily Incoming vs Outgoing

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API Hourly Incoming vs Outgoing

Peak at 200k+ threaded DependencyCommand executions/second

Peak at 30k+ incoming requests/second

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35This view of the dashboard was captured during a latency monkey simulation to test resilience against latency (http://techblog.netflix.com/2011/07/netflix-simian-army.html) and shows how several of the DependencyCommands degraded in health and showed timeouts, threadpool rejections, short-circuiting and failures.

The DependencyCommands of dependencies not affected by latency were unaffected.

During this test no users were prevented from using Netflix on any devices. Instead fallbacks and graceful degradation occurred and as soon as latency was removed all systems returned to health within seconds.

36This was another latency monkey simulation that affected a single DependencyCommand.

Peak at 100M+ incoming requests (30k+/second)

Success drops off, Timeouts and Short Circuiting shed load

Latency spikes from ~30ms median to first 2000+ then 10000+ ms

37These graphs show the full duration of a latency monkey simulation (and look similar to real production events) when latency occurred and the DependencyCommand timed-out and short-circuited the requests and returned fallbacks.

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Fallback.Fail silent.Fail fast.

Shed load.

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Netflix API

Dependency A

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40Second half of the presentation discusses architectural changes to enable optimizing the API for each Netflix device as opposed to a generic one-size-fits-all API which treats all devices the same.

Single Network Request from Clients(use LAN instead of WAN)

landing page requires ~dozen API requests

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41The one-size-fits-all API results in chatty clients, some requiring ~dozen requests to render a page.

Single Network Request from Clients(use LAN instead of WAN)

some clients are limited in the number ofconcurrent network connections

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Single Network Request from Clients(use LAN instead of WAN)

network latency makes this even worse(mobile, home, wifi, geographic distance, etc)

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Single Network Request from Clients(use LAN instead of WAN)

push call pattern to server ...

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44The client should make a single request and push the 'chatty' part to the server where low-latency networks and multi-core servers can perform the work far more efficiently.

Single Network Request from Clients(use LAN instead of WAN)

... and eliminate redundant calls

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Send Only The Bytes That Matter(optimize responses for each client)

part of client now on server

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47The client now extends over the network barrier and runs a portion in the server itself. The client sends requests over HTTP to its other half running in the server which then can access a Java API at a very granular level to access exactly what it needs and return an optimized response suited to the devices exact requirements and user experience.

Send Only The Bytes That Matter(optimize responses for each client)

client retrieves and delivers exactly what theirdevice needs in its optimal format

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Send Only The Bytes That Matter(optimize responses for each client)

interface is now a Java API that clientinteracts with at a granular level

Netflix API

Service Layer

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Netflix API

Service Layer

Client Client

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Leverage Concurrency(but abstract away its complexity)

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Leverage Concurrency(but abstract away its complexity)

no synchronized, volatile, locks, Futures orAtomic*/Concurrent* classes in client-server code

Netflix API

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51Concurrency is abstracted away behind an asynchronous API and data is retrieved, transformed and composed using high-order-functions (such as map, mapMany, merge, zip, take, toList, etc). Groovy is used for its closure support that lends itself well to the functional programming style.

Functional Reactive Programmingcomposable asynchronous functions

Fully asynchronous API - Clients can’t block

def video1Call = api.getVideos(api.getUser(), 123456, 7891234);def video2Call = api.getVideos(api.getUser(), 6789543);

// higher-order functions used to compose asynchronous calls togetherwx.merge(video1Call, video2Call).toList().subscribe([ onNext: { listOfVideos -> for(video in listOfVideos) { response.getWriter().println("video: " + video.id + " " + video.title); } }, onError: { exception -> response.setStatus(500); response.getWriter().println("Error: " + exception.getMessage()); }])

Service calls are all asynchronous

Functional programming

with higher-order functions

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Request Collapsingbatch don’t burst

53The DependencyCommand resilience layer is leveraged for concurrency including optimizations such as request collapsing (automated batching) which bundles bursts of calls to the same service into batches without the client code needing to understand or manually optimize for batching. This is particularly important when client code becomes highly concurrent and data is requested in multiple different code paths sometimes written by different engineers. Request collapsing automatically captures and batches the calls together. The collapsing functionality also supports sharded architectures so a batch of requests can be sharded into sub-batches if the client-server relationship requires requests to be routed to a sharded backend.

Request Collapsingbatch don’t burst

100:1 collapsing ratio (batch size of ~100)54

This graph shows an extreme example of a dependency where we collapse requests at a ratio of 100:1

Request Collapsingbatch don’t burst

100:1 collapsing ratio (batch size of ~100)

4000 rps instead of 400,000 rps

55This is the same graph but on a power scale instead of linear so the blue line (actual network requests) shows up.

Request Scoped Caching short-lived and concurrency aware

56Another use of the DependencyCommand layer is to allow client code to perform requests without concern of duplicate network calls due to concurrency.

The Futures is atomically cached using “putIfAbsent” in the request scope shared via ThreadLocals of each thread so clients can request data in multiple code paths without inefficiency concerns.

Request Cachingstateless

57Some examples of request caching de-duplicating backend calls. On some the impact is reasonably high while on most it is a small percentage or none at all but overall provided a measurable drop in network calls and in some use cases for client code significantly improved latency by eliminating unnecessary network calls.

Optimize for each device. Leverage the server.

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58The Netflix API is becoming a platform that empowers user-interface teams to build their own API endpoints that are optimized to their client applications and devices.

Fault Tolerance in a High Volume, Distributed Systemhttp://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

Making the Netflix API More Resilienthttp://techblog.netflix.com/2011/12/making-netflix-api-more-resilient.html

Embracing the Differences : Inside the Netflix API Redesignhttp://techblog.netflix.com/2012/07/embracing-differences-inside-netflix.html

Ben Christensen@benjchristensen

http://www.linkedin.com/in/benjchristensen

Netflix is Hiringhttp://jobs.netflix.com

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