From 6908225e30eaf8e2fa359a129959921a57b580c1 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Tue, 2 Sep 2025 14:51:39 +0200 Subject: [PATCH 1/6] Add README.md --- README.md | 155 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 155 insertions(+) diff --git a/README.md b/README.md index e69de29..9d40985 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,155 @@ +# DataFusion Distributed + +Library that brings distributed execution capabilities to [DataFusion](https://github.com/apache/datafusion). + +> [!WARNING] +> This project is currently under construction and is not yet ready for production use. + +## What can you do with this crate? + +This crate is a toolkit that extends [DataFusion](https://github.com/apache/datafusion) with distributed capabilities, +providing a developer experience as close as possible to vanilla DataFusion and being opinionated about the networking +stack used for hosting the different workers involved in a query. + +Users of this library can expect to take their existing single-node DataFusion-based systems and add distributed +capabilities with minimal changes. + +## Core tenets of the project + +- Be as close as possible to vanilla DataFusion, providing a seamless integration with existing DataFusion systems and + a familiar API for building applications. +- Unopinionated networking. This crate does not take any opinion about the networking stack, and users are expected + to leverage their own infrastructure for hosting DataFusion nodes. +- No coordinator-worker architecture. To keep infrastructure simple, any node can act as a coordinator or a worker. + +## Architecture + +A distributed DataFusion query is executed in a very similar fashion as a normal DataFusion query with one key +difference: + +The physical plan is divided into stages that can be executed in different machines, and exchange data using Arrow +Flight. All of this is done at the physical plan level, and is implemented as a `PhysicalOptimizerRule` that: + +1. Inspects the non-distributed plan, placing network boundaries (`ArrowFlightReadExec` nodes) in the appropriate places +2. Based on the placed network boundaries, divides the plan into stages and assigns tasks to them. + +For example, imagine we have a plan that looks like this: + +``` + ┌──────────────────────┐ + │ ProjectionExec │ + └──────────────────────┘ + ▲ + ┌──────────┴───────────┐ + │ AggregateExec │ + │ (final) │ + └──────────────────────┘ + ▲ + ┌──────────┴───────────┐ + │ RepartionExec │ + │ (3 input partitions) │ + └──────────────────────┘ + ▲ + ┌──────────┴───────────┐ + │ AggregateExec │ + │ (partial) │ + └──────────────────────┘ + ▲ + ┌──────────┴───────────┐ + │ DataSourceExec │ + └──────────────────────┘ +``` + +We want to distribute the aggregation to something like this: + +``` + ┌──────────────────────┐ + │ ProjectionExec │ + └──────────────────────┘ + ▲ + ┌──────────┴───────────┐ + │ AggregateExec │ + │ (final) │ + └──────────────────────┘ + ▲ ▲ ▲ + ┌────────────────────────┘ │ └─────────────────────────┐ + ┌──────────┴───────────┐ ┌──────────┴───────────┐ ┌───────────┴──────────┐ + │ AggregateExec │ │ AggregateExec │ │ AggregateExec │ + │ (partial) │ │ (partial) │ │ (partial) │ + └──────────────────────┘ └──────────────────────┘ └──────────────────────┘ + ▲ ▲ ▲ + ┌──────────┴───────────┐ ┌──────────┴───────────┐ ┌───────────┴──────────┐ + │ DataSourceExec │ │ DataSourceExec │ │ DataSourceExec │ + └──────────────────────┘ └──────────────────────┘ └──────────────────────┘ +``` + +The first step is to place the `ArrowFlightReadExec` network boundary in the appropriate place (the following drawing +shows the partitioning scheme in each node): + +``` + ┌──────────────────────┐ + │ ProjectionExec │ + └─────────[0]──────────┘ + ▲ + ┌──────────┴───────────┐ + │ AggregateExec │ + │ (final) │ + └─────────[0]──────────┘ + ▲ + ┌──────────┴───────────┐ + │ ArrowFlightRead │ <- this node was injected to tell the distributed planner + │ (3 input tasks) │ that there must be a network boundary here. + └──────[0][1][2]───────┘ + ▲ ▲ ▲ + ┌───────┴──┴──┴────────┐ + │ AggregateExec │ + │ (partial) │ + └──────[0][1][2]───────┘ + ▲ ▲ ▲ + ┌───────┴──┴──┴────────┐ + │ DataSourceExec │ + └──────[0][1][2]───────┘ +``` + +Based on that boundary, the plan is divided into stages and tasks are assigned to each stage: + +``` + ┌────── (stage 2) ───────┐ + │┌──────────────────────┐│ + ││ ProjectionExec ││ + │└──────────┬───────────┘│ + │┌──────────┴───────────┐│ + ││ AggregateExec ││ + ││ (final) ││ + │└──────────┬───────────┘│ + │┌──────────┴───────────┐│ + ││ ArrowFlightReadExec ││ + │└──────[0][1][2]───────┘│ + └─────────▲─▲─▲──────────┘ + ┌────────────────────────┘ │ └─────────────────────────┐ + │ │ │ + ┌─── task 0 (stage 1) ───┐ ┌── task 1 (stage 1) ────┐ ┌── task 2 (stage 1) ────┐ + │ │ │ │ │ │ │ │ │ + │┌─────────[0]──────────┐│ │┌─────────[0]──────────┐│ │┌──────────[0]─────────┐│ + ││ AggregateExec ││ ││ AggregateExec ││ ││ AggregateExec ││ + ││ (partial) ││ ││ (partial) ││ ││ (partial) ││ + │└──────────┬───────────┘│ │└──────────┬───────────┘│ │└───────────┬──────────┘│ + │┌─────────[0]──────────┐│ │┌─────────[0]──────────┐│ │┌──────────[0]─────────┐│ + ││ DataSourceExec ││ ││ DataSourceExec ││ ││ DataSourceExec ││ + │└──────────────────────┘│ │└──────────────────────┘│ │└──────────────────────┘│ + └────────────────────────┘ └────────────────────────┘ └────────────────────────┘ +``` + +The plan is immediately executable, and the same process that planned the distributed query can start executing the head +stage (stage 2). The `ArrowFlightReadExec` in that stage will know from which tasks to gather data from stage 1, and +will issue 3 concurrent Arrow Flight requests to the appropriate physical nodes. + +This means that: + +1. The head stage is executed normally as if the query was not distributed. +2. Upon calling `.execute()` on the `ArrowFlightReadExec`, instead of recursively calling `.execute()` on its children, + they will be serialized and sent over the wire to another node. +3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick up + the request containing the serialized chunk of the overall plan, and execute it. +4. This is repeated for each stage, and data will start flowing from bottom to top until it reaches the head stage. + From 3484a57e68a205970430c839efe9d43555ae3c73 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Tue, 2 Sep 2025 15:03:47 +0200 Subject: [PATCH 2/6] Add apache 2.0 license --- LICENSE.txt | 201 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 LICENSE.txt diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..8f4ef8f --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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From d31308b5ff44d2273ba4b3f20e1728ae3e76f561 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Tue, 2 Sep 2025 15:11:56 +0200 Subject: [PATCH 3/6] Add development guidelines --- README.md | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 75 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9d40985..52af6ea 100644 --- a/README.md +++ b/README.md @@ -149,7 +149,80 @@ This means that: 1. The head stage is executed normally as if the query was not distributed. 2. Upon calling `.execute()` on the `ArrowFlightReadExec`, instead of recursively calling `.execute()` on its children, they will be serialized and sent over the wire to another node. -3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick up - the request containing the serialized chunk of the overall plan, and execute it. +3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick + up the request containing the serialized chunk of the overall plan, and execute it. 4. This is repeated for each stage, and data will start flowing from bottom to top until it reaches the head stage. +## Development + +### Prerequisites + +- Rust 1.85.1 or later (specified in `rust-toolchain.toml`) +- Git LFS for test data + +### Setup + +1. **Clone the repository:** + ```bash + git clone git@github.com:datafusion-contrib/datafusion-distributed + cd datafusion-distributed + ``` + +2. **Install Git LFS and fetch test data:** + ```bash + git lfs install + git lfs checkout + ``` + +### Running Tests + +**Unit and integration tests:** + +```bash +cargo test --features integration +``` + +### Running Examples + +**Start localhost workers:** + +```bash +# Terminal 1 +cargo run --example localhost_worker -- 8080 --cluster-ports 8080,8081 + +# Terminal 2 +cargo run --example localhost_worker -- 8081 --cluster-ports 8080,8081 +``` + +**Execute distributed queries:** + +```bash +cargo run --example localhost_run -- 'SELECT count(*) FROM weather' --cluster-ports 8080,8081 +``` + +### Benchmarks + +**Generate TPC-H benchmark data:** + +```bash +cd benchmarks +./gen-tpch.sh +``` + +**Run TPC-H benchmarks:** + +```bash +cargo run -p datafusion-distributed-benchmarks --release -- tpch --path benchmarks/data/tpch_sf1 +``` + +### Project Structure + +- `src/` - Core library code + - `flight_service/` - Arrow Flight service implementation + - `plan/` - Physical plan extensions and operators + - `stage/` - Execution stage management + - `common/` - Shared utilities +- `examples/` - Usage examples +- `tests/` - Integration tests +- `benchmarks/` - Performance benchmarks +- `testdata/` - Test datasets From 0704ba411e4e480a7b211f20d6caef9d8d439737 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Tue, 2 Sep 2025 15:28:15 +0200 Subject: [PATCH 4/6] Improve README.md --- README.md | 36 +++++++++++++++++++++++++++++------- 1 file changed, 29 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 52af6ea..a4eb758 100644 --- a/README.md +++ b/README.md @@ -8,8 +8,8 @@ Library that brings distributed execution capabilities to [DataFusion](https://g ## What can you do with this crate? This crate is a toolkit that extends [DataFusion](https://github.com/apache/datafusion) with distributed capabilities, -providing a developer experience as close as possible to vanilla DataFusion and being opinionated about the networking -stack used for hosting the different workers involved in a query. +providing a developer experience as close as possible to vanilla DataFusion while being unopinionated about the +networking stack used for hosting the different workers involved in a query. Users of this library can expect to take their existing single-node DataFusion-based systems and add distributed capabilities with minimal changes. @@ -18,8 +18,8 @@ capabilities with minimal changes. - Be as close as possible to vanilla DataFusion, providing a seamless integration with existing DataFusion systems and a familiar API for building applications. -- Unopinionated networking. This crate does not take any opinion about the networking stack, and users are expected - to leverage their own infrastructure for hosting DataFusion nodes. +- Unopinionated about networking. This crate does not take any opinion about the networking stack, and users are + expected to leverage their own infrastructure for hosting DataFusion nodes. - No coordinator-worker architecture. To keep infrastructure simple, any node can act as a coordinator or a worker. ## Architecture @@ -27,7 +27,7 @@ capabilities with minimal changes. A distributed DataFusion query is executed in a very similar fashion as a normal DataFusion query with one key difference: -The physical plan is divided into stages that can be executed in different machines, and exchange data using Arrow +The physical plan is divided into stages that can be executed on different machines and exchange data using Arrow Flight. All of this is done at the physical plan level, and is implemented as a `PhysicalOptimizerRule` that: 1. Inspects the non-distributed plan, placing network boundaries (`ArrowFlightReadExec` nodes) in the appropriate places @@ -111,7 +111,8 @@ shows the partitioning scheme in each node): └──────[0][1][2]───────┘ ``` -Based on that boundary, the plan is divided into stages and tasks are assigned to each stage: +Based on that boundary, the plan is divided into stages and tasks are assigned to each stage. Each task will be +responsible for the different partitions in the original plan. ``` ┌────── (stage 2) ───────┐ @@ -150,9 +151,30 @@ This means that: 2. Upon calling `.execute()` on the `ArrowFlightReadExec`, instead of recursively calling `.execute()` on its children, they will be serialized and sent over the wire to another node. 3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick - up the request containing the serialized chunk of the overall plan, and execute it. + up the request containing the serialized chunk of the overall plan and execute it. 4. This is repeated for each stage, and data will start flowing from bottom to top until it reaches the head stage. +## Getting familiar with distributed DataFusion + +There are some runnable examples showcasing how to provide a localhost implementation for Distributed DataFusion in +[examples/](examples): + +- [localhost_worker.rs](examples/localhost_worker.rs): code that spawns an Arrow Flight Endpoint listening for physical + plans over the network. +- [localhost_run.rs](examples/localhost_run.rs): code that distributes a query across the spawned Arrow Flight Endpoints + and executes it. + +The integration tests also provide an idea about how to use the library and what can be achieved with it: + +- [tpch_validation_test.rs](tests/tpch_validation_test.rs): executes all TPCH queries and performs assertions over the + distributed plans. +- [custom_config_extension.rs](tests/custom_config_extension.rs): showcases how to propagate custom DataFusion config + extensions. +- [custom_extension_codec.rs](tests/custom_extension_codec.rs): showcases how to propagate custom physical extension + codecs. +- [distributed_aggregation.rs](tests/distributed_aggregation.rs): showcases how to manually place `ArrowFlightReadExec` + nodes in a plan and build a distributed query out of it. + ## Development ### Prerequisites From c1b6d3c669799206caf5a628c0120bdf4b526d63 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Wed, 3 Sep 2025 09:49:57 +0200 Subject: [PATCH 5/6] Improve README.md --- README.md | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index a4eb758..069147c 100644 --- a/README.md +++ b/README.md @@ -24,10 +24,20 @@ capabilities with minimal changes. ## Architecture +Before diving into the architecture, it's important to clarify some terms and what they mean: + +- `worker`: a physical machine listening to serialized plans over an Arrow Flight interface. +- `network boundary`: a node in the plan that streams data from a network interface rather than directly from its + children. Implemented as an `ArrowFlightReadExec` physical DataFusion node. +- `stage`: a portion of the plan separated by a network boundary from other parts of the plan. Implemented as any + other physical node in DataFusion. +- `task`: a unit of work inside a stage that executes a subset of its partitions in a specific worker. +- `subplan`: a slice of the overall plan + A distributed DataFusion query is executed in a very similar fashion as a normal DataFusion query with one key difference: -The physical plan is divided into stages that can be executed on different machines and exchange data using Arrow +The physical plan is divided into stages that can be executed on different workers and exchange data using Arrow Flight. All of this is done at the physical plan level, and is implemented as a `PhysicalOptimizerRule` that: 1. Inspects the non-distributed plan, placing network boundaries (`ArrowFlightReadExec` nodes) in the appropriate places @@ -111,7 +121,7 @@ shows the partitioning scheme in each node): └──────[0][1][2]───────┘ ``` -Based on that boundary, the plan is divided into stages and tasks are assigned to each stage. Each task will be +Based on that boundary, the plan is divided into stages, and tasks are assigned to each stage. Each task will be responsible for the different partitions in the original plan. ``` @@ -131,11 +141,11 @@ responsible for the different partitions in the original plan. │ │ │ ┌─── task 0 (stage 1) ───┐ ┌── task 1 (stage 1) ────┐ ┌── task 2 (stage 1) ────┐ │ │ │ │ │ │ │ │ │ - │┌─────────[0]──────────┐│ │┌─────────[0]──────────┐│ │┌──────────[0]─────────┐│ + │┌─────────[0]──────────┐│ │┌─────────[1]──────────┐│ │┌──────────[2]─────────┐│ ││ AggregateExec ││ ││ AggregateExec ││ ││ AggregateExec ││ ││ (partial) ││ ││ (partial) ││ ││ (partial) ││ │└──────────┬───────────┘│ │└──────────┬───────────┘│ │└───────────┬──────────┘│ - │┌─────────[0]──────────┐│ │┌─────────[0]──────────┐│ │┌──────────[0]─────────┐│ + │┌─────────[0]──────────┐│ │┌─────────[1]──────────┐│ │┌──────────[2]─────────┐│ ││ DataSourceExec ││ ││ DataSourceExec ││ ││ DataSourceExec ││ │└──────────────────────┘│ │└──────────────────────┘│ │└──────────────────────┘│ └────────────────────────┘ └────────────────────────┘ └────────────────────────┘ @@ -149,7 +159,7 @@ This means that: 1. The head stage is executed normally as if the query was not distributed. 2. Upon calling `.execute()` on the `ArrowFlightReadExec`, instead of recursively calling `.execute()` on its children, - they will be serialized and sent over the wire to another node. + the child subplan will be serialized and sent over the wire to another node. 3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick up the request containing the serialized chunk of the overall plan and execute it. 4. This is repeated for each stage, and data will start flowing from bottom to top until it reaches the head stage. @@ -167,7 +177,7 @@ There are some runnable examples showcasing how to provide a localhost implement The integration tests also provide an idea about how to use the library and what can be achieved with it: - [tpch_validation_test.rs](tests/tpch_validation_test.rs): executes all TPCH queries and performs assertions over the - distributed plans. + distributed plans and the results vs running the queries in single node mode with a small scale factor. - [custom_config_extension.rs](tests/custom_config_extension.rs): showcases how to propagate custom DataFusion config extensions. - [custom_extension_codec.rs](tests/custom_extension_codec.rs): showcases how to propagate custom physical extension From d7026d3cc5553d36fec6413efda4193043e3b814 Mon Sep 17 00:00:00 2001 From: Gabriel Musat Mestre Date: Wed, 3 Sep 2025 15:46:58 +0200 Subject: [PATCH 6/6] Improve README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 069147c..f97e48e 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ capabilities with minimal changes. Before diving into the architecture, it's important to clarify some terms and what they mean: -- `worker`: a physical machine listening to serialized plans over an Arrow Flight interface. +- `worker`: a physical machine listening to serialized execution plans over an Arrow Flight interface. - `network boundary`: a node in the plan that streams data from a network interface rather than directly from its children. Implemented as an `ArrowFlightReadExec` physical DataFusion node. - `stage`: a portion of the plan separated by a network boundary from other parts of the plan. Implemented as any @@ -37,8 +37,8 @@ Before diving into the architecture, it's important to clarify some terms and wh A distributed DataFusion query is executed in a very similar fashion as a normal DataFusion query with one key difference: -The physical plan is divided into stages that can be executed on different workers and exchange data using Arrow -Flight. All of this is done at the physical plan level, and is implemented as a `PhysicalOptimizerRule` that: +The physical plan is divided into stages, each stage is assigned tasks that run in parallel in different workers. All of +this is done at the physical plan level, and is implemented as a `PhysicalOptimizerRule` that: 1. Inspects the non-distributed plan, placing network boundaries (`ArrowFlightReadExec` nodes) in the appropriate places 2. Based on the placed network boundaries, divides the plan into stages and assigns tasks to them. @@ -158,8 +158,8 @@ will issue 3 concurrent Arrow Flight requests to the appropriate physical nodes. This means that: 1. The head stage is executed normally as if the query was not distributed. -2. Upon calling `.execute()` on the `ArrowFlightReadExec`, instead of recursively calling `.execute()` on its children, - the child subplan will be serialized and sent over the wire to another node. +2. Upon calling `.execute()` on `ArrowFlightReadExec`, instead of propagating the `.execute()` call on its child, + the subplan is serialized and sent over the wire to be executed on another worker. 3. The next node, which is hosting an Arrow Flight Endpoint listening for gRPC requests over an HTTP server, will pick up the request containing the serialized chunk of the overall plan and execute it. 4. This is repeated for each stage, and data will start flowing from bottom to top until it reaches the head stage.