jacques-n commented on a change in pull request #35: ARROW-5971: [Website] Blog
post introducing Arrow Flight
URL: https://github.com/apache/arrow-site/pull/35#discussion_r334700123
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File path: _posts/2019-09-30-introducing-arrow-flight.md
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+---
+layout: post
+title: "Introducing Apache Arrow Flight: A Framework for Fast Data Transport"
+description: "This post introduces Arrow Flight, a framework for building high
+performance data services. We have been building Flight over the last 18 months
+and are looking for developers and users to get involved."
+date: "2019-10-13 00:00:00 -0600"
+author: Wes McKinney
+categories: [application]
+---
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+
+Over the last 18 months, the Apache Arrow community has been busy designing and
+implementing **Flight**, a new general-purpose client-server framework to
+simplify high performance transport of large datasets over network interfaces.
+
+Flight initially is focused on optimized transport of the Arrow columnar format
+(i.e. "Arrow record batches") over [gRPC][1], Google's popular HTTP/2-based
+general-purpose RPC library and framework. While we have focused on integration
+with gRPC, as a development framework Flight is not intended to be exclusive to
+gRPC.
+
+In the 0.15.0 Apache Arrow release, we have ready-to-use Flight implementations
+in C++ (with Python bindings) and Java. These libraries are suitable for beta
+users who are comfortable being on the bleeding edge while we continue to
+refine some low-level details in the Flight internals.
+
+## Motivation
+
+Many people have experienced the pain associated with accessing large datasets
+over a network. There are many different transfer protocols and tools for
+reading datasets from remote data services, such as ODBC and JDBC. Over the
+last 10 years, file-based data warehousing in formats like CSV, Avro, and
+Parquet has become popular, but this also presents challenges as raw data must
+be transferred to local hosts before being deserialized.
+
+The work we have done since the beginning of Apache Arrow holds exciting
+promise for accelerating data transport in a number of ways. The [Arrow
+columnar format][2] has key features that can help us:
+
+* It is an "on-the-wire" representation of tabular data that does not require
+ deserialization on receipt
+* Its natural mode is that of "streaming batches", larger datasets are
+ transported a batch of rows at a time (called "record batches" in Arrow
+ parlance). In this post we will talk about "data streams", these are
+ sequences of Arrow record batches using the project's binary protocol
+* The format is language-independent and now has library support in 11
+ languages and counting.
+
+Implementations of standard protocols like ODBC generally implement their own
+custom on-wire binary protocols that must be marshalled to and from each
+library's public interface. The performance of ODBC or JDBC libraries varies
+greatly from case to case.
+
+Our design goal for Flight is to create a new protocol for data services that
+uses the Arrow columnar format as both the over-the-wire data representation as
+well as the public API presented to developers. In doing so, we reduce or
+remove the serialization costs associated with data transport and increase the
+overall efficiency of distributed data systems. Additionally, two systems that
+are already using Apache Arrow for other purposes can communicate data to each
+other with extreme efficiency.
+
+## Flight Basics
+
+The Arrow Flight libraries provide a development framework for implementing a
+service that can send and receive data streams. A Flight server supports
+several basic kinds of requests:
+
+* **Handshake**: a simple request to determine whether the client is authorized
+ and, in some cases, to establish an implementation-defined session token to
+ use for future requests
+* **ListFlights**: return a list of available data streams
+* **GetSchema**: return the schema for a data stream
+* **GetFlightInfo**: return a "access plan" for a dataset of interest, possibly
+ requiring consuming multiple data streams. This request can accept custom
+ serialized commands containing, for example, your specific application
+ parameters.
+* **DoGet**: send a data stream to a client
+* **DoPut**: receive a data stream from a client
+* **DoAction**: perform an implementation-specific action and return any
+ results, i.e. a generalized function call
+* **ListActions**: return a list of available action types
+
+We take advantage of gRPC's elegant "bidirectional" streaming support to allow
+clients and servers to send data and metadata to each other simultaneously
+while requests are being served.
+
+A simple Flight setup might consist of a single server to which clients connect
+and make `DoGet` requests.
+
+<div align="center">
+<img src="{{ site.baseurl }}/img/20191014_flight_simple.png"
+ alt="Flight Simple Architecture"
+ width="50%" class="img-responsive">
+</div>
+
+## Optimizing Data Throughput over gRPC
+
+While using a general-purpose messaging library like gRPC has numerous specific
+benefits beyond the obvious ones (taking advantage of all the engineering that
+Google has done on the problem), some work was needed to improve the
+performance of transporting large datasets. Many kinds of gRPC users only deal
+with relatively small messages, for example.
+
+The best-supported way to use gRPC is to define services in a [Protocol
+Buffers][3] (aka "Protobuf") `.proto` file. A Protobuf plugin for gRPC
+generates gRPC service stubs that you can use to implement your
+applications. RPC commands and data messages are serialized using the [Protobuf
+wire format][4]. Because we use "vanilla gRPC and Protocol Buffers", gRPC
+clients that are ignorant of the Arrow columnar format can still interact with
+Flight services and handle the Arrow data opaquely.
+
+The main data-related Protobuf type in Flight is called `FlightData`. Reading
+and writing Protobuf messages in general is not free, so we implemented some
+low-level optimizations in gRPC in both C++ and Java to do the following:
+
+* Generate the Protobuf wire format for `FlightData` including the Arrow record
+ batch being sent without going through any intermediate memory copying or
+ serialization steps.
+* Reconstruct a Arrow record batch from the Protobuf representation of
+ `FlightData` without any memory copying or deserialization. In fact, we
+ intercept the encoded data payloads without allowing the Protocol Buffers
+ library to touch them.
+
+In a sense we are "having our cake and eating it, too". Flight implementations
+having these optimizations will have better performance, while naive gRPC
+clients can still talk to the Flight service and use a Protobuf library to
+deserialize `FlightData` (albeit with some performance penalty).
+
+As far as absolute speed, in our C++ data throughput benchmarks, we are seeing
+end-to-end TCP throughput in excess of 2-3GB/s on localhost without TLS
+enabled. This benchmark shows a transfer of ~12 gigabytes of data in about 4
+seconds:
+
+
+```shell
+$ ./arrow-flight-benchmark --records_per_stream 100000000
+Bytes read: 12800000000
+Nanos: 3900466413
+Speed: 3129.63 MB/s
+```
+
+From this we can conclude that the machinery of Flight and gRPC adds relatively
+little overhead, and it suggests that many real-world applications of Flight
+will be bottlenecked on network bandwidth.
+
+## Horizontal Scalability: Parallel and Partitioned Data Access
+
+Many distributed database-type systems make use of an architectural pattern
+where the results of client requests are routed through a "coordinator" and
+sent to the client. Aside from the obvious efficiency issues of transporting a
+dataset multiple times on its way to a client, it also presents a scalability
+problem for getting access to very large datasets.
+
+We wanted Flight to enable systems to create horizontally scalable data
+services without having to deal with such bottlenecks. A client request to a
+dataset using the `GetFlightInfo` RPC returns a list of **endpoints**, each of
+which contains a server location and a **ticket** to send that server in a
+`DoGet` request to obtain a part of the full dataset. To get access to the
+entire dataset, all of the endpoints must be consumed. While Flight streams are
+not necessarily ordered, we provide for application-defined metadata which can
+be used to serialize ordering information.
+
+This multiple-endpoint pattern has a number of benefits:
+
+* Endpoints can be read by clients in parallel.
+* The service that serves the `GetFlightInfo` "planning" request can delegate
+ work to sibling services to take advantage of data locality or simply to help
+ with load balancing.
+* Nodes in a distributed cluster can take on different roles. For example, a
+ subset of nodes might be responsible for planning queries while other nodes
+ exclusively fulfill data stream (`DoGet` or `DoPut`) requests.
+
+Here is an example diagram of a multi-node architecture with split service
+roles:
+
+<div align="center">
+<img src="{{ site.baseurl }}/img/20191014_flight_complex.png"
+ alt="Flight Complex Architecture"
+ width="60%" class="img-responsive">
+</div>
+
+## Actions: Extending Flight with application business logic
+
+While the `GetFlightInfo` request supports sending opaque serialized commands
+when requesting a dataset, a client may need to be able to ask a server to
+perform other kinds of operations. For example, a client may request for a
+particular dataset to be "pinned" in memory so that subsequent requests from
+other clients are served faster.
+
+A Flight service can thus optionally define "actions" which are carried out by
+the `DoAction` RPC. An action request contains the name of the action being
+performed and optional serialized data containing further needed
+information. The result of an action is a gRPC stream of opaque binary results.
+
+An example action would be the command `'ListDatasets'` which could return a
+stream of dataset names that are available on that server.
+
+Note that it is not required for a server to implement any actions, and actions
+need not return results.
+
+## Encryption and Authentication
+
+Flight supports encryption out of the box using gRPC's built in TLS / OpenSSL
+capabilities.
+
+For authentication, there are extensible authentication handlers for the client
+and server that permit simple authentication schemes (like user and password)
+as well as more involved authentication such as Kerberos. The Flight protocol
+comes with a built-in `BasicAuth` so that user/password authentication can be
+implemented out of the box without custom development.
+
+## Middleware and Tracing
+
+gRPC has the concept of "interceptors" which have allowed us to develop
+developer-defined "middleware" that can provide instrumentation of or telemetry
+for incoming and outgoing requests. One such framework for such instrumentation
+is [OpenTracing][6].
+
+Note that middleware functionality is one of the newest areas of the project
+and is only currently available in the project's master branch.
+
+## gRPC, but not only gRPC
+
+We specify server locations for `DoGet` requests using RFC 3986 compliant
+URIs. For example, TLS-secured gRPC may be specified like
+`grpc+tls://$HOST:$PORT`.
+
+While we think that using gRPC for the "command" layer of Flight servers makes
+sense, we may wish to support data transport layers other than TCP such as
+[RDMA][7]. While some design and development work is required to make this
+possible, the idea is that gRPC could be used to coordinate get and put
+transfers which may be carried out on protocols other than TCP.
+
+## Getting Started and What's Next
Review comment:
I think it would be helpful to link to some real world examples. Two that we
have are:
Connector to access Dremio using Arrow Flight instead of ODBC:
https://github.com/dremio-hub/dremio-flight-connector
20-50x performance improvements
Arrow Flight Data source for Apache Spark
https://github.com/rymurr/flight-spark-source
@rymurr, can you add a readme for the second link?
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