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Bistro is a flexible distributed scheduler, a high-performance framework supporting multiple paradigms while retaining ease of configuration, management, and monitoring.

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Bistro: A fast, flexible toolkit for scheduling and running distributed tasks

Build Status

This README is a very abbreviated introduction to Bistro. Visit http://facebook.github.io/bistro for a more structured introduction, and for the docs.

Bistro is a toolkit for making distributed computation systems. It can schedule and run distributed tasks, including data-parallel jobs. It enforces resource constraints for worker hosts and data-access bottlenecks. It supports remote worker pools, low-latency batch scheduling, dynamic shards, and a variety of other possibilities. It has command-line and web UIs.

Some of the diverse problems that Bistro solved at Facebook:

  • Safely run map-only ETL tasks against live production databases (MySQL, HBase, Postgres).
  • Provide a resource-aware job queue for batch CPU/GPU compute jobs.
  • Replace Hadoop for a periodic online data compression task on HBase, improving time-to-completion and reliability by over 10x.

You can run Bistro "out of the box" to suit a variety of different applications, but even so, it is a tool for engineers. You should be able to get started just by reading the documentation, but when in doubt, look at the code --- it was written to be read.

Some applications of Bistro may involve writing small plugins to make it fit your needs. The code is built to be extensible. Ask for tips, and we'll do our best to help. In return, we hope that you will send a pull request to allow us to share your work with the community.

Early release

Although Bistro has been in production at Facebook for over 3 years, the present public release is partial, including just the server components.

Install the dependencies and build

Bistro needs a 64-bit Linux, Folly, FBThrift, Proxygen, boost, and libsqlite3. You need 2-3GB of RAM to build, as well as GCC 4.9 or above.

build/README.md documents the usage of Docker-based scripts that build Bistro on Ubuntu 14.04, 16.04, and Debian 8.6. You should be able to follow very similar steps on most modern Linux distributions.

If you run into dependency problems, look at bistro/cmake/setup.cmake for a full list of Bistro's external dependencies (direct and indirect). We gratefully accept patches that improve Bistro's builds, or add support for various flavors of Linux and Mac OS.

The binaries will be in bistro/cmake/{Debug,Release}. Available build targets are explained here: http://cmake.org/Wiki/CMake_Useful_Variables#Compilers_and_Tools You can start Bistro's unit tests by running ctest in those directories.

Your first Bistro run

This is just one simple demo, but Bistro is a very flexible tool. Refer to http://facebook.github.io/bistro/ for more in-depth information.

We are going to start a single Bistro scheduler talking to one 'remote' worker.

Aside: The scheduler tracks jobs, and data shards on which to execute them. It also makes sure only to start new tasks when the required resources are available. The remote worker is a module for executing centrally scheduled work on many machines. The UI can aggregate many schedulers at once, so using remote workers is optional --- a share-nothing, many-scheduler system is sometimes preferable.

Let's make a task to execute:

cat <<EOF > ~/demo_bistro_task.sh
#!/bin/bash
echo "I got these arguments: \$@"
echo "stderr is also logged" 1>&2
echo "done" > "\$2"  # Report the task status to Bistro via a named pipe
EOF
chmod u+x ~/demo_bistro_task.sh

Open two terminals, one for the scheduler, and one for the worker.

# In both terminals
cd bistro/bistro
# Start the scheduler in one terminal
./cmake/Debug/server/bistro_scheduler \
  --server_port=6789 --http_server_port=6790 \
  --config_file=scripts/test_configs/simple --clean_statuses \
  --CAUTION_startup_wait_for_workers=1 --instance_node_name=scheduler
# Start the worker in another
mkdir /tmp/bistro_worker
./cmake/Debug/worker/bistro_worker --server_port=27182 --scheduler_host=:: \
  --scheduler_port=6789 --worker_command="$HOME/demo_bistro_task.sh" \
  --data_dir=/tmp/bistro_worker

You should be seeing some lively log activity on both terminals. In several seconds, the worker-scheduler negotiation should complete, and you should see messages like "Task ... quit with status" and "Got status".

Since we passed --clean_statuses, the scheduler will not persist any task completions that happened during this run. The worker, on the other hand, will keep a record of the task logs in /tmp/bistro_worker/task_logs.sql3.

If you want task completions to persist across runs, tell Bistro where to put the SQLite database, via --data_dir=/tmp/bistro_scheduler and --status_table=task_statuses

mkdir /tmp/bistro_scheduler
./cmake/Debug/server/bistro_scheduler \
  --server_port=6789 --http_server_port=6790 \
  --config_file=scripts/test_configs/simple \
  --data_dir=/tmp/bistro_scheduler --status_table=task_statuses \
  --CAUTION_startup_wait_for_workers=1 --instance_node_name=scheduler

You can query the running scheduler via its REST API:

curl -d '{"a":{"handler":"jobs"},"b":{"handler":"running_tasks"}}' :::6790
curl -d '{"my subquery":{"handler":"task_logs","log_type":"stdout"}}' :::6790

Pro-tip: For ease of reading, pipe the output through either jq or json_pp (from a Perl package). For longer outputs, try | jq -C . | less -R.

You should also take a look at the scheduler configuration to see how its jobs, nodes, and resources were specified.

less scripts/test_configs/simple

For debugging, we typically invoke the binaries like this:

gdb cmake/Debug/worker/bistro_worker -ex "r ..." 2>&1 | tee WORKER.txt

When configuring a real deployment, be sure to carefully review the --help of the scheduler & worker binaries, as well as the documentation on http://facebook.github.io/bistro. And don't hesitate to ask for help in the group: https://www.facebook.com/groups/bistro.scheduler

License

See LICENSE.

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Bistro is a flexible distributed scheduler, a high-performance framework supporting multiple paradigms while retaining ease of configuration, management, and monitoring.

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