Seastar

Introduction

SeaStar is an event-driven framework allowing you to write non-blocking, asynchronous code in a relatively straightforward manner (once understood). It is based on futures.

Building Seastar

Building seastar on Fedora 21

Installing required packages:

yum install gcc-c++ libaio-devel ninja-build ragel hwloc-devel numactl-devel libpciaccess-devel cryptopp-devel xen-devel boost-devel

You then need to run the following to create the "build.ninja" file:

./configure.py

Note it is enough to run this once, and you don't need to repeat it before every build. build.ninja includes a rule which will automatically re-run ./configure.py if it changes.

Then finally:

ninja-build

Building seastar on Fedora 20

Installing GCC 4.9 for gnu++1y:

• Beware that this installation will replace your current GCC version.

yum install fedora-release-rawhide
yum --enablerepo rawhide update gcc-c++
yum --enablerepo rawhide install libubsan libasan

Installing required packages:

yum install libaio-devel ninja-build ragel hwloc-devel numactl-devel libpciaccess-devel cryptopp-devel

You then need to run the following to create the "build.ninja" file:

./configure.py

Note it is enough to run this once, and you don't need to repeat it before every build. build.ninja includes a rule which will automatically re-run ./configure.py if it changes.

Then finally:

ninja-build

Building seastar on Ubuntu 14.04

Installing required packages:

sudo apt-get install libaio-dev ninja-build ragel libhwloc-dev libnuma-dev libpciaccess-dev libcrypto++-dev libboost-all-dev

Installing GCC 4.9 for gnu++1y. Unlike the Fedora case above, this will not harm the existing installation of GCC 4.8, and will install an additional set of compilers, and additional commands named gcc-4.9, g++-4.9, etc., that need to be used explicitly, while the "gcc", "g++", etc., commands continue to point to the 4.8 versions.

# Install add-apt-repository
sudo apt-get install software-properties-common python-software-properties
# Use it to add Ubuntu's testing compiler repository
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
# Install gcc 4.9 and relatives
sudo apt-get install g++-4.9
# Also set up necessary header file links and stuff (?)
sudo apt-get install gcc-4.9-multilib g++-4.9-multilib

To compile Seastar explicitly using gcc 4.9, use:

./configure.py --compiler=g++-4.9

To compile OSv explicitly using gcc 4.9, use:

make CC=gcc-4.9 CXX=g++-4.9 -j 24

Building seastar in Docker container

To build a Docker image:

docker build -t seastar-dev docker/dev

Create an shell function for building insider the container (bash syntax given):

$ seabuild() { docker run -v $HOME/seastar/:/seastar -u $(id -u):$(id -g) -w /seastar -t seastar-dev "$@"; }

(it is recommended to put this inside your .bashrc or similar)

To build inside a container:

$ seabuild ./configure.py
$ seabuild ninja-build

Building with a DPDK network backend

1. Setup host to compile DPDK:
   • Ubuntu

sudo apt-get install -y build-essential linux-image-extra-`uname -r` 

2. Prepare a DPDK SDK:

• Download the latest DPDK release: wget http://dpdk.org/browse/dpdk/snapshot/dpdk-1.8.0.tar.gz
• Untar it.
• Edit config/common_linuxapp: set CONFIG_RTE_MBUF_REFCNT to 'n'.
• For DPDK 1.7.x: edit config/common_linuxapp:
  • Set CONFIG_RTE_LIBRTE_PMD_BOND to 'n'.
  • Set CONFIG_RTE_MBUF_SCATTER_GATHER to 'n'.
  • Set CONFIG_RTE_LIBRTE_IP_FRAG to 'n'.
• Start the tools/setup.sh script as root.
• Compile a linuxapp target (option 9).
• Install IGB_UIO module (option 11).
• Bind some physical port to IGB_UIO (option 17).
• Configure hugepage mappings (option 14/15).

3. Run a configure.py: ./configure.py --dpdk-target <Path to untared dpdk-1.8.0 above>/x86_64-native-linuxapp-gcc --compiler=g++-4.9.
4. Run ninja-build.

To run with the DPDK backend for a native stack give the seastar application --dpdk-pmd 1 parameter.

Futures and promises

A future is a result of a computation that may not be available yet. Examples include:

• a data buffer that we are reading from the network
• the expiration of a timer
• the completion of a disk write
• the result computation that requires the values from one or more other futures.

a promise is an object or function that provides you with a future, with the expectation that it will fulfill the future.

Promises and futures simplify asynchronous programming since they decouple the event producer (the promise) and the event consumer (whoever uses the future). Whether the promise is fulfilled before the future is consumed, or vice versa, does not change the outcome of the code.

Consuming a future

You consume a future by using its then() method, providing it with a callback (typically a lambda). For example, consider the following operation:

future<int> get();   // promises an int will be produced eventually
future<> put(int)    // promises to store an int

void f() {
    get().then([] (int value) {
        put(value + 1).then([] {
            std::cout << "value stored successfully\n";
        });
    });
}

Here, we initiate a get() operation, requesting that when it completes, a put() operation will be scheduled with an incremented value. We also request that when the put() completes, some text will be printed out.

Chaining futures

If a then() lambda returns a future (call it x), then that then() will return a future (call it y) that will receive the same value. This removes the need for nesting lambda blocks; for example the code above could be rewritten as:

future<int> get();   // promises an int will be produced eventually
future<> put(int)    // promises to store an int

void f() {
    get().then([] (int value) {
        return put(value + 1);
    }).then([] {
        std::cout << "value stored successfully\n";
    });
}

Loops

Loops are achieved with a tail call; for example:

future<int> get();   // promises an int will be produced eventually
future<> put(int)    // promises to store an int

future<> loop_to(int end) {
    if (value == end) {
        return make_ready_future<>();
    }
    get().then([end] (int value) {
        return put(value + 1);
    }).then([end] {
        return loop_to(end);
    });
}

The make_ready_future() function returns a future that is already available --- corresponding to the loop termination condition, where no further I/O needs to take place.

Under the hood

When the loop above runs, both then method calls execute immediately --- but without executing the bodies. What happens is the following:

1. get() is called, initiates the I/O operation, and allocates a temporary structure (call it f1).
2. The first then() call chains its body to f1 and allocates another temporary structure, f2.
3. The second then() call chains its body to f2.

Again, all this runs immediately without waiting for anything.

After the I/O operation initiated by get() completes, it calls the continuation stored in f1, calls it, and frees f1. The continuation calls put(), which initiates the I/O operation required to perform the store, and allocates a temporary object f12, and chains some glue code to it.

After the I/O operation initiated by put() completes, it calls the continuation associated with f12, which simply tells it to call the continuation associated with f2. This continuation simply calls loop_to(). Both f12 and f2 are freed. loop_to() then calls get(), which starts the process all over again, allocating new versions of f1 and f2.

Handling exceptions

If a .then() clause throws an exception, the scheduler will catch it and cancel any dependent .then() clauses. If you want to trap the exception, add a .then_wrapped() clause at the end:

future<buffer> receive();
request parse(buffer buf);
future<response> process(request req);
future<> send(response resp);

void f() {
    receive().then([] (buffer buf) {
        return process(parse(std::move(buf));
    }).then([] (response resp) {
        return send(std::move(resp));
    }).then([] {
        f();
    }).then_wrapped([] (auto&& f) {
        try {
            f.get();
        } catch (std::exception& e) {
            // your handler goes here
        }
    });
}

The previous future is passed as a parameter to the lambda, and its value can be inspected with f.get(). When the get() variable is called as a function, it will re-throw the exception that aborted processing, and you can then apply any needed error handling. It is essentially a transformation of

buffer receive();
request parse(buffer buf);
response process(request req);
void send(response resp);

void f() {
    try {
        while (true) {
            auto req = parse(receive());
            auto resp = process(std::move(req));
            send(std::move(resp));
        }
    } catch (std::exception& e) {
        // your handler goes here
    }
}

Note, however, that the .then_wrapped() clause will be scheduled both when exception occurs or not. Therefore, the mere fact that .then_wrapped() is executed does not mean that an exception was thrown. Only the execution of the catch block can guarantee that.

This is shown below:

future<my_type> my_future();

void f() {
    receive().then_wrapped([] (future<my_type> f) {
        try {
            my_type x = f.get();
            return do_something(x);
        } catch (std::exception& e) {
            // your handler goes here
        }
    });
}

Setup notes

SeaStar is a high performance framework and tuned to get the best performance by default. As such, we're tuned towards polling vs interrupt driven. Our assumption is that applications written for SeaStar will be busy handling 100,000 IOPS and beyond. Polling means that each of our cores will consume 100% cpu even when no work is given to it.

Recommended hardware configuration for SeaStar

• CPUs - As much as you need. SeaStar is highly friendly for multi-core and NUMA
• NICs - As fast as possible, we recommend 10G or 40G cards. It's possible to use 1G to but you may be limited by their capacity. In addition, the more hardware queue per cpu the better for SeaStar. Otherwise we have to emulate that in software.
• Disks - Fast SSDs with high number of IOPS.
• Client machines - Usually a single client machine can't load our servers. Both memaslap (memcached) and WRK (httpd) cannot over load their matching server counter parts. We recommend running the client on different machine than the servers and use several of them.