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Arrow Flight RPC 101 | GoodData

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

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

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

Now, even when the Flight RPC specification is brief, it took us a while to understand and apply it – not as a result of it’s difficult 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 further data on high of what’s within the official documentation.

The Flight abstraction

The Flight RPC makes use of the ‘Flight’ abstraction to symbolize ‘some information’. Every flight has a Flight Descriptor – which basically tells both ‘what’ information 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 – by way of 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 type 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’ll be able to have flight paths that appear to be ‘trainingData/‘ and your service would interpret this as “this can be a piece of coaching information and it has some distinctive identifier”. The service can deal with this as semantic data and behave accordingly.

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

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


Command descriptors specify the flight – e.g., the information – utilizing an arbitrary payload {that a} information service can perceive and based mostly on which it could “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 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 fancy JSON or Protobuf message serialized into bytes.

For instance, you might have a service that may run an SQL SELECT on some information 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 information 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 information providers.

Studying information

With Flight RPC, purchasers ought to get the Flight information 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. Purchasers name the GetFlightInfo and supply the flight descriptor – so this incorporates both path or command:

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

Ultimately, the FlightInfo incorporates the next data:

  • Endpoints (or partitions), that make up the flight information.
  • Areas inside every endpoint, the place replicas are saved.
  • A ticket for every endpoint the shopper should use to learn the information from the out there areas.
  • Arrow schema describing the information. (optionally available)
  • Knowledge dimension. (optionally available)

The endpoints and areas are fairly simple: they describe information 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 introduced 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 best piece of information.

Now that shopper code has the FlightInfo, it could proceed to the best areas to get information for the completely different endpoints by making a DoGet name – both serially or in parallel, this actually is determined by the shopper code.

The DoGet will open a stream of Arrow information. It is very important word that the stream consists of the schema in each batch of information – so even when the preliminary GetFlightInfo name for no matter motive 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 optionally available, most 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 exhausting 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 precious particularly when the system has a number of cooperating information providers.

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

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

This might then work out on this order:

  • The ‘question‘ information 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 information from the ‘cache‘ service
    • If it doesn’t exist, the ‘cache‘ service runs the mandatory question, serves the information instantly and create the cache within the background.

Observe that there are various the reason why the ‘question‘ service wouldn’t discover cached information. Naturally, there may be the cache-miss situation, however other than that the ‘question’ service could also be accessing a real-time information supply the place caching is undesirable or the caching will not be potential in any respect as a result of compliance necessities.

Both method, the shopper doesn’t care. The shopper is desirous about some information 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 – usually easy, standalone information providers.

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

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

Writing information

When purchasers need to write information to a service, they use the DoPut technique.

The DoPut accepts FlightDescriptor after which opens a bi-directional stream between the server and the shopper. Via this stream, the shopper can ship Arrow information to put in writing and obtain responses from the server.

With DoPut, you should use descriptors containing a flight path to put in writing. The everyday use case here’s a service that caches or shops information that the shopper ‘by some means’ obtains and needs 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 will not be enough to deal with extra complicated use circumstances – take for example parallel add of a number of information partitions.

In such circumstances, your information providers should implement extra “Customized Actions” that the shopper will use on high of the DoPut.

For instance, your information service can have StartParallelUpload to provoke and FinishParallelUpload to finalize the parallel add of a knowledge set. When you’d name StartParallelUpload, your purchasers would do as many parallel DoPut calls as needed (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 information service can have some customized necessities that can’t be addressed by the prevailing Flight RPC strategies. To accommodate for this, the Flight RPC lets you ‘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 information operations that contain a number of DoPut/DoGet calls, you should use them for administering the service, implementing well being checks, or enhancing maintainability.

The infrastructure takes care of the transport considerations 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.

Much 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, additionally it is good to understand that some Flight RPC sorts – corresponding to FlightDescriptor – are additionally serializable and could possibly be used for motion physique or consequence; this may be helpful in case your motion is instantly associated to the flight entity itself.

An instance from our analytics stack: We’ve a customized motion that tells purchasers the place to carry out DoPut. The shopper calls the customized motion with the identical FlightDescriptor they might use for DoPut itself. The results of this practice motion is a listing of areas that the shopper ought to write to.

Offloading Compute

Aside from supporting information reads and writes, the Flight RPC additionally has the DoExchange operation which your providers can supply to the purchasers in order that they’ll offload computation.

The utilization is fairly simple:

  • The shopper calls DoExchange with FlightDescriptor; this can usually comprise a command with payload describing the compute.
  • The shopper streams information 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 shouldn’t have any information providers that supply the DoExchange for purchasers. We’ve, nevertheless, discovered it very useful in multi-process providers that require inter-process communication.

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

Working ‘pandas a service’ will get difficult for a lot of causes – a giant one lies in Python itself: the International 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 corresponding to elevated latencies, failing well being checks, and/or failing liveness probes.

To unravel this, we now have designed our Pandas information 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 information, it can offload the computation to the employee course of.

The server finds the enter information, initiates DoExchange with the employee, streams the enter information 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 might elevate on completely different events – the infrastructure will handle error propagation between the server and the shopper.

You will discover the ‘normal’ set of exceptions corresponding to Unauthenticated, Unauthorized, ServerError, InternalError, UnavailableError, and others.

What we now 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 strong error dealing with methods.

Fortunately the error dealing with in Flight RPC can be extensible. Whereas it isn’t potential to to plug in arbitrary error sorts, it’s potential to connect extra, customized data to the prevailing errors.

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

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

The purchasers 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 a payload hooked up, the shopper might be sure that there’s something actually incorrect 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 be taught a bit extra in regards to the Flight RPC and the assorted methods it may be used and prolonged.

From my nearly two 12 months 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 information providers that work with information in Arrow format.

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

The massive promoting level can be the prevailing client-server infrastructure offered by the Apache Arrow mission – you shouldn’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 be taught extra?

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

Different elements of the sequence are in regards to the Constructing of the Fashionable Knowledge Service Layer, Undertaking 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’ll be able to 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 initiatives, however we additionally need to permit exterior builders to deploy their providers into our platform. In the end we’re interested by open-sourcing the Longbow. Would you be desirous about it? Tell us, your opinion issues!

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

Wish to see how nicely all of it works in follow, you’ll be able to attempt the GoodData free trial! Or should you’d prefer to attempt our new experimental options enabled by this new strategy (AI, Machine Studying and rather more), be happy to enroll in our Labs Surroundings.



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