Repository: mesos
Updated Branches:
  refs/heads/master 83f81b7b2 -> e7244ae1e


Added a performance working group December 2017 blog post.

This blog post discusses the master failover performance improvements
that were made in the past few months.


Project: http://git-wip-us.apache.org/repos/asf/mesos/repo
Commit: http://git-wip-us.apache.org/repos/asf/mesos/commit/e7244ae1
Tree: http://git-wip-us.apache.org/repos/asf/mesos/tree/e7244ae1
Diff: http://git-wip-us.apache.org/repos/asf/mesos/diff/e7244ae1

Branch: refs/heads/master
Commit: e7244ae1eb84a8bfcbe2940107c7f97a53832cf2
Parents: 83f81b7
Author: Benjamin Mahler <[email protected]>
Authored: Mon Dec 11 11:52:43 2017 -0800
Committer: Benjamin Mahler <[email protected]>
Committed: Mon Dec 11 11:52:43 2017 -0800

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+---
+layout: post
+title: December 2017 Performance Working Group Progress Report
+published: true
+post_author:
+  display_name: Benjamin Mahler
+  gravatar: fb43656d4d45f940160c3226c53309f5
+  twitter: bmahler
+tags: Performance
+---
+
+**Scalability and performance are key features for Mesos. Some users of Mesos 
already run production clusters that consist of more than 35,000+ agents and 
100,000+ active tasks.** However, there remains a lot of room for improvement 
across a variety of areas of the system.
+
+The performance working group was created in order to focus on some of these 
areas. The group's charter is to improve scalability / throughput / latency 
across the system, and in order to measure our improvements and prevent 
performance regressions we will write benchmarks and automate them.
+
+In the past few months, we've focused on making improvements to the following 
areas:
+
+* **Master failover time-to-completion**: Achieved a 450-600% improvement in 
throughput, which reduces the time-to-completion by 80-85%.
+* 
**[Libprocess](https://github.com/apache/mesos/tree/master/3rdparty/libprocess) 
message passing throughput**: These improvements will be covered in a separate 
blog post.
+
+Before we dive into the master failover improvements, I would like to 
recognize and thank the following contributors:
+
+* **Dmitry Zhuk**: for writing *a lot* of patches for improving the master 
failover performance.
+* **Michael Park**: for reviewing and shipping many of Dmitry's more 
challenging patches.
+* **Yan Xu**: for writing the master failover benchmark that was the basis for 
measuring the improvements.
+
+## Master Failover Time-To-Completion
+
+Our first area of focus was to improve the time it takes for a master failover 
to complete, where completion is defined as all of the agents successfully 
re-registering. Mesos is architected to use a centralized master with standby 
masters that participate in a quorum for high availability. For scalability 
reasons, the leading master stores the state of the cluster in-memory. During a 
master failover, the leading master needs to therefore re-build the in-memory 
state from all of the agents that re-register. During this time, the master is 
available to process other requests, but will be exposing only partial state to 
API consumers.
+
+The rebuilding of the master’s in-memory state can be expensive for larger 
clusters, and so the focus of this effort was to improve the efficiency of 
this. Improvements were made via several areas, and only the highest-impact 
changes are listed below:
+
+### Protobuf 3.5.0 Move Support
+
+We upgraded to protobuf 3.5.0 in order to gain move support. When we profiled 
the master, we found that it spent a lot of time copying protobuf messages 
during agent re-registration. This support allowed us to eliminate copies of 
protobuf messages while retaining value semantics.
+
+### Move Support and Copy Elimination in Libprocess `dispatch` / `defer` / 
`install`
+
+Libprocess provides several primitives for message passing:
+
+* `dispatch`: Provides the ability to post a messages to a local `Process`
+* `defer`: Provides a deferred `dispatch`. i.e. a function object that when 
invoked will issue a `dispatch`.
+* `install`: Installs a handler for receiving a protobuf message.
+
+These primitives did not have move support, as they were originally added 
prior to the addition of C++11 support to the code-base. In order to eliminate 
copies, we enhanced these primitives to support moving arguments in and out.
+
+This required introducing a new C++ utility, because `defer` takes on the same 
API as `std::bind` (e.g., placeholders). Specifically, the function object 
returned by `std::bind` does not move the bound arguments into the stored 
callable. In order to enable this, `defer` now uses a utility we introduced 
called `lambda::partial` rather than `std::bind`. `lambda::partial` performs 
partial function application similar to `std::bind` except the returned 
function object moves the bound arguments into the stored callable if the 
invocation is performed on an r-value function object.
+
+### Copy Elimination in the Master
+
+With these previous enhancements in place, we were able to eliminate many of 
the expensive copies of protobuf messages performed by the master.
+
+### Benchmark and Results
+
+We wrote a synthetic benchmark to simulate a master failover. This benchmark 
prepares all the messages that would be sent to the master by the agents that 
need to re-register:
+
+* The benchmark uses synthetic agents in that they are just an actor that 
knows how to re-register with the master.
+* Each "agent" will send a configurable number of active and completed tasks 
belonging to a configurable number of active and completed frameworks.
+* Each task has 10 small labels to introduce metadata overhead.
+
+The benchmark has a few caveats:
+
+* It does not use executors (this would show improved results over what is 
shown below, but for simplicity the benchmark omits them)
+* It uses local message passing, whereas a real cluster would be passing 
messages over HTTP.
+* It uses a quorum size of 1, so writes to the master’s registry occur only 
on single local log replica.
+* The synthetic agents do not retry their re-registration, whereas typically 
agents will retry with a backoff.
+
+This was tested on a 2015 Macbook Pro with 2.8 GHz Intel Core i7 processor. 
Mesos was configured using: `Apple LLVM version 9.0.0 (clang-900.0.38)`, with 
`-O2` enabled in 1.5.0.
+
+The first results represent a cluster with 10 active tasks per agent across 5 
frameworks, with no completed tasks. The results from 1,000 - 40,000 agents 
with 10,000 - 400,000 active tasks:
+
+![1.3 - 1.5 Master Failover without Task History 
Graph](/assets/img/documentation/1.3-1.5_master_failover_no_history.png)
+
+There was a reduction in the time-to-completion of ~80% due to a 450-500% 
improvement in throughput across 1.3.0 to 1.5.0.
+
+The second results add task history: each agent also now contains 100 
completed tasks across 5 completed frameworks. The results from 1,000 - 40,000 
agents with 10,000 - 400,000 active tasks and 100,000 - 4,000,000 completed 
tasks are shown below:
+
+![1.3 - 1.5 Master Failover with Task History 
Graph](/assets/img/documentation/1.3-1.5_master_failover_with_history)
+
+This represents a reduction in time-to-completion of ~85% due to a 550-700% 
improvement in throughput across 1.3.0 to 1.5.0.
+
+## Performance Working Group Roadmap
+
+We're currently targeting the following areas for improvements:
+
+* **Performance of the v1 API**: Currently the v1 API can be significantly 
slower than the v0 API. We would like to reach parity, and ideally surpass the 
performance of the v0 API.
+  * 
**[Libprocess](https://github.com/apache/mesos/tree/master/3rdparty/libprocess) 
HTTP performance**: This will be undertaken as part of improving the v1 API 
performance, since it is HTTP-based.
+* **Master state API performance**: Currently, API queries of the master's 
state are serviced by the same master actor that processes all of the messages 
from schedulers and agents. Since the query processing can block the master 
from processing other events, users need to be careful not to query the master 
excessively. In practice, the master gets queried quite heavily due to the 
presence of several tools that rely on the master's state (e.g. DNS tooling, 
UIs, CLIs, etc) and so this is a critical problem for users. This effort will 
leverage the state streaming API to stream the state to a different actor that 
can serve the state API requests. This will ensure that expensive state queries 
do not affect the master's ability to process events.
+
+If you are a user and would like to suggest some areas for performance 
improvement, please let us know by emailing <[email protected]>.

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