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Arrow Flight RPC 101 | GoodData

That is a part of a collection about FlexQuery and the Longbow engine powering it.

On this article I’ll stroll you thru the Flight RPC, which is a basic a part of our Longbow engine, which I describe within the Venture Longbow article.

Flight RPC is an API tailor-made for knowledge providers. It may be used to implement completely different providers – the same old suspects: producers, shoppers, transformers, and all the pieces in between. It’s constructed on gRPC and comes with ready-made and performance-optimized infrastructure – you wouldn’t have to care in regards to the technicalities of streaming knowledge in or out of the providers.

Now, even when the Flight RPC specification is brief, it took us a while to know and apply it – not as a result of it’s sophisticated or overly complicated, however as a result of we needed to use it appropriately. Within the following sections, I’ll attempt to clarify some key Flight RPC ideas in layman’s phrases and supply additional data on prime of what’s within the official documentation.

The Flight abstraction

The Flight RPC makes use of the ‘Flight’ abstraction to symbolize ‘some knowledge’. Every flight has a Flight Descriptor – which primarily tells both ‘what’ knowledge to get or ‘how’ to get the information. Flight RPC comes with two subtypes of flight descriptors: path descriptor (what) and command descriptor (how).


Path descriptors specify the flight – the information – through its “flight path.” You’ll be able to view this as a path-like identifier of the information. That’s, the flight path doesn’t essentially should be some sort of opaque identifier – it’s one thing that the service can parse and alter its processing accordingly. Flight RPC doesn’t put any constraints on what ought to or shouldn’t be within the flight path – it’s fully as much as the implementation to determine.

For instance, you possibly can have flight paths that appear like ‘trainingData/‘ and your service would interpret this as “this can be a piece of coaching knowledge and it has some distinctive identifier”. The service can deal with this as semantic data and behave accordingly.

Or one other instance, you possibly can have flight paths that appear like ‘my_user1/trainingData/‘ and your service would interpret this as: This knowledge belongs to my_user1, it’s coaching knowledge, and it has this distinctive identifier.

The flights described by a flight path can be utilized to work with materialized knowledge, and the paths can carry semantic data.


Command descriptors specify the flight – e.g., the information – utilizing an arbitrary payload {that a} knowledge service can perceive and primarily based on which it will possibly “by some means” produce (or, within the parlance of Flight RPC, “generate”) or eat the information.

The Flight RPC doesn’t care how the command appears to be like or what it incorporates. From Flight RPC’s perspective, the command is a byte string – it’s as much as your providers to know and take care of it. The command could also be something from a easy string saying “do it” or a posh JSON or Protobuf message serialized into bytes.

For instance, you could have a service that may run an SQL SELECT on some knowledge supply. You’ll be able to design the payload for that service as a JSON containing the information supply’s URL, SQL assertion textual content, and SQL parameters. Your knowledge service receives a request to get the flight described by this payload. The code parses & validates the enter after which proceeds with operating the SQL.

You’ll be able to view instructions as payloads used to invoke your customized knowledge providers.

Studying knowledge

With Flight RPC, shoppers ought to get the Flight knowledge by first calling the GetFlightInfo after which utilizing the returned FlightInfo to truly learn the information utilizing a DoGet name.

Right here is the place issues get attention-grabbing. Shoppers name the GetFlightInfo and supply the flight descriptor – so this incorporates both path or command:

  • For flight paths, the server sometimes returns particulars the place to entry the materialized knowledge
  • For instructions, the GetFlightInfo name is definitely the service invocation – this when the place the service ought to carry out all of the work mandatory to provide the information

Ultimately, the FlightInfo incorporates the next data:

  • Endpoints (or partitions), that make up the flight knowledge.
  • Areas inside every endpoint, the place replicas are saved.
  • A ticket for every endpoint the shopper should use to learn the information from the accessible places.
  • Arrow schema describing the information. (elective)
  • Information dimension. (elective)

The endpoints and places are fairly simple: they describe knowledge partitions and for every partition, there’s a listing of replicas.

However what’s the ticket? From the Flight RPC perspective, it’s an opaque byte string that must be offered on the location to truly learn the information. So equally to the instructions, your providers can put absolutely anything in there – so long as the content material permits the server to stream the suitable piece of information.

Now that shopper code has the FlightInfo, it will possibly proceed to the suitable places to get knowledge for the completely different endpoints by making a DoGet name – both serially or in parallel, this actually depends upon the shopper code.

The DoGet will open a stream of Arrow knowledge. You will need to observe that the stream contains the schema in each batch of information – so even when the preliminary GetFlightInfo name for no matter cause doesn’t return a schema, the shopper will know the form of the information on the time it will get the information.

Whereas the Arrow schema is elective, lots of the Flight RPC implementations require that it’s all the time included within the FlightInfo. We discovered that in some providers it may be actually laborious to provide schema on the time of GetFlightInfo precisely and so when the implementation requires the schema, our code sends an empty schema with a metadata marker.

Advantages of a cohesive system

The layer of indirection between GetFlightInfo and the DoGet may be very useful particularly when the system has a number of cooperating knowledge providers.

It may be helpful for instance to implement gateways or clear caching. Think about two providers:

A ‘question*’* service to question knowledge from a database and a ‘cache‘ service that may retailer materialized knowledge underneath explicit flight paths.

This could then work out on this order:

  • The ‘question‘ knowledge service accepts GetFlightInfo for a command
  • The ‘question‘ checks whether or not a flight path with the cached consequence already exists.
    • If it exists: the ‘question‘ returns FlightInfo that navigates the shopper to learn the materialized knowledge from the ‘cache‘ service
    • If it doesn’t exist, the ‘cache‘ service runs the required question, serves the information immediately and create the cache within the background.

Notice that there are numerous explanation why the ‘question‘ service wouldn’t discover cached knowledge. Naturally, there’s the cache-miss state of affairs, however other than that the ‘question’ service could also be accessing a real-time knowledge supply the place caching is undesirable or the caching is probably not attainable in any respect because of compliance necessities.

Both approach, the shopper doesn’t care. The shopper is curious about some knowledge and doesn’t care the place it will get it from. A system with appropriately designed GetFlightInfo, FlightInfo, and tickets permits this.


The indirection of GetFlightInfo -> DoGet strategies could also be cumbersome and even pointless for some providers – sometimes easy, standalone knowledge providers.

In these instances, it’s attainable to ‘bend’ the Flight RPC to simplify issues – whereas nonetheless benefiting from the prevailing shopper and server infrastructure offered by the Apache Arrow venture.

Let’s take for instance a fundamental single-node service that simply hosts some knowledge and permits shoppers to learn it in a single stream. For such a service, you possibly can fully ignore the GetFlightInfo and solely use DoGet. The ticket that shoppers should cross to the DoGet can include the payload essential to establish the information to stream. The payload could be something. It could be a easy identifier of the information or a structured payload.

Writing knowledge

When shoppers need to write knowledge to a service, they use the DoPut methodology.

The DoPut accepts FlightDescriptor after which opens a bi-directional stream between the server and the shopper. By way of this stream, the shopper can ship Arrow knowledge to jot down and obtain responses from the server.

With DoPut, you should use descriptors containing a flight path to jot down. The everyday use case here’s a service that caches or shops knowledge that the shopper ‘by some means’ obtains and desires to entry later.

Doing DoPut with a descriptor that incorporates a command can be utilized to implement extra complicated writes – for instance, performing bulk writes of information into a knowledge warehouse. On this case, the command payload would carry the assertion to execute.

Advanced utilization

The essential use of DoPut is pretty easy and easy. Nonetheless, by itself, it is probably not enough to deal with extra complicated use instances – take for example parallel add of a number of knowledge partitions.

In such instances, your knowledge providers must implement further “Customized Actions” that the shopper will use on prime of the DoPut.

For instance, your knowledge service can have StartParallelUpload to provoke and FinishParallelUpload to finalize the parallel add of a knowledge set. When you’d name StartParallelUpload, your shoppers would do as many parallel DoPut calls as mandatory (to create the partitions or endpoints within the parlance of Flight RPC) after which in spite of everything partitions have been uploaded, you’d name FinishParallelUpload to finalize the add.

Custom Actions

As a rule, your knowledge service may have some customized necessities that can not be addressed by the prevailing Flight RPC strategies. To accommodate for this, the Flight RPC means that you can ‘plug in’ new arbitrary actions.

You should use these for something your providers want. For instance, you should use the customized actions throughout extra complicated knowledge operations that contain a number of DoPut/DoGet calls, you should use them for administering the service, implementing well being checks, or bettering maintainability.

The infrastructure takes care of the transport issues and your code can give attention to the motion logic itself – assigning the motion names and optionally designing the motion physique and motion consequence and the way they need to be serialized.

Just like command descriptors or tickets, the motion physique and consequence construction and serialization are as much as you. A typical selection is both to make use of JSON or Protocol Buffers.

Nonetheless, it is usually good to remember that some Flight RPC varieties – resembling FlightDescriptor – are additionally serializable and may very well be used for motion physique or consequence; this may be helpful in case your motion is immediately associated to the flight entity itself.

An instance from our analytics stack: We now have a customized motion that tells shoppers the place to carry out DoPut. The shopper calls the customized motion with the identical FlightDescriptor they’d use for DoPut itself. The results of this tradition motion is an inventory of places that the shopper ought to write to.

Offloading Compute

Aside from supporting knowledge reads and writes, the Flight RPC additionally has the DoExchange operation which your providers can provide to the shoppers in order that they will offload computation.

The utilization is fairly simple:

  • The shopper calls DoExchange with FlightDescriptor; this may sometimes include a command with payload describing the compute.
  • The shopper streams knowledge in.
  • The server performs the transformation.
  • The shopper reads the consequence.

That is all achieved utilizing a single DoExchange name and a single bi-directional stream ready by the Flight RPC infrastructure.

DoExchange for inter-process compute offloading

In our analytics stack, we wouldn’t have any knowledge providers that supply the DoExchange for shoppers. We now have, nonetheless, discovered it very useful in multi-process providers that require inter-process communication.

Considered one of our Python knowledge providers permits shoppers to generate new flights by performing manipulation utilizing the Pandas dataframe library.

Operating ‘pandas a service’ will get tough for a lot of causes – an enormous one lies in Python itself: the World Interpreter Lock (GIL). For a lot of operations Pandas holds the GIL and does CPU-intensive work – successfully ‘taking time’ the server must do different work. On busy servers, this could result in nasty issues resembling elevated latencies, failing well being checks, and/or failing liveness probes.

To unravel this, now we have designed our Pandas knowledge service in order that it spawns a number of employee processes. Every course of runs its personal Flight RPC server listening on a Unix socket. When the server receives a request to generate knowledge, it should offload the computation to the employee course of.

The server finds the enter knowledge, initiates DoExchange with the employee, streams the enter knowledge to the employee, after which waits for the outcomes, which it then streams out.


Flight RPC and its infrastructure include a predefined set of errors that the server could elevate on completely different events – the infrastructure will care for error propagation between the server and the shopper.

You can find the ‘common’ set of exceptions resembling Unauthenticated, Unauthorized, ServerError, InternalError, UnavailableError, and others.

What now we have discovered whereas constructing a extra complicated system with Flight RPC is that on their very own, these built-in errors usually are not sufficient to implement extra sturdy error dealing with methods.

Fortunately the error dealing with in Flight RPC can also be extensible. Whereas it isn’t attainable to to plug in arbitrary error varieties, it’s attainable to connect further, customized data to the prevailing errors.

Just like instructions or tickets, the errors may also include a customized binary payload the place your server can put no matter it needs – like a serialized Protocol Buffer message.

So for instance in our case, all our providers are contracted to lift Flight RPC errors with this tradition binary payload hooked up. The payload is a protocol buffer message with an error code and extra error particulars.

The shoppers all the time search for this hooked up payload and can deserialize and carry out error dealing with in keeping with the error code included within the message. If there isn’t any payload hooked up, the shopper could be sure that there’s something actually improper on the server as a result of errors with out our customized payload can solely ever be raised by the Flight RPC infrastructure itself earlier than our server code is even concerned.

Wrapping Up

I hope this text helped you study a bit extra in regards to the Flight RPC and the varied methods it may be used and prolonged.

From my nearly two yr expertise of working and designing in opposition to Flight RPC, I can wholeheartedly advocate you to make use of it in case you are planning to construct knowledge providers that work with knowledge in Arrow format.

The Flight RPC, whereas considerably opinionated, nonetheless provides you a variety of freedom to both bend or prolong it to match your wants. Additionally, the opinionated components are stable and are literally one thing you can begin appreciating as you construct extra complicated providers or a set of providers.

The large promoting level can also be the prevailing client-server infrastructure offered by the Apache Arrow venture – you wouldn’t have to design and construct your individual and as a substitute depend on the optimized infrastructure developed by the group.

Final however not least, you should use Apache Arrow in a dozen languages, from low-level, like Cpp and Rust to high-level, like Python and JavaScript.

Want to study extra?

As we talked about within the introduction, that is a part of a collection of articles, the place we take you on a journey of how we constructed our new analytics stack on prime of Apache Arrow and what we realized about it within the course of.

Different components of the collection are in regards to the Constructing of the Fashionable Information Service Layer, Venture Longbow, particulars in regards to the versatile storage and caching, and final however not least, how good the DuckDB quacks with Apache Arrow!

As you possibly can see within the article, we’re opening our platform to an exterior viewers. We not solely use (and contribute to) state-of-the-art open-source tasks, however we additionally need to enable exterior builders to deploy their providers into our platform. Finally we’re desirous about open-sourcing the Longbow. Would you be curious about it? Tell us, your opinion issues!

Should you’d like to debate our analytics stack (or anything), be happy to affix our Slack group!

Need to see how nicely all of it works in apply, you possibly can attempt the GoodData free trial! Or should you’d prefer to attempt our new experimental options enabled by this new method (AI, Machine Studying and rather more), be happy to enroll in our Labs Setting.

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