flink-http-connector

The HTTP TableLookup connector that allows for pulling data from external system via HTTP GET method and HTTP Sink that allows for sending data to external system via HTTP requests.

Note: The main branch may be in an unstable or even broken state during development. Please use releases instead of the main branch in order to get a stable set of binaries.

The goal for HTTP TableLookup connector was to use it in Flink SQL statement as a standard table that can be later joined with other stream using pure SQL Flink.

Currently, HTTP source connector supports only Lookup Joins (TableLookup) [1] in Table/SQL API. HttpSink supports both Streaming API (when using HttpSink built using HttpSinkBuilder) and the Table API (using connector created in HttpDynamicTableSinkFactory).

Prerequisites

Java 11
Maven 3
Flink 1.15+

Runtime dependencies

This connector has few Flink's runtime dependencies, that are expected to be provided.

org.apache.flink.flink-java
org.apache.flink.flink-clients
org.apache.flink.flink-connector-base

Installation

In order to use the flink-http-connector the following dependencies are required for both projects using a build automation tool (such as Maven or SBT) and SQL Client with SQL JAR bundles. For build automation tool reference, look into Maven Central: https://mvnrepository.com/artifact/com.getindata/flink-http-connector.

Documentation

You can read the official JavaDoc documentation of the latest release at https://javadoc.io/doc/com.getindata/flink-http-connector.

Usage

HTTP TableLookup Source

Flink SQL table definition:

Enrichment Lookup Table

CREATE TABLE Customers (
    id STRING,
    id2 STRING,
    msg STRING,
    uuid STRING,
    details ROW<
      isActive BOOLEAN,
      nestedDetails ROW<
        balance STRING
      >
    >
) WITH (
'connector' = 'rest-lookup',
'format' = 'json',
'url' = 'http://localhost:8080/client',
'asyncPolling' = 'true'
)

Data Source Table

CREATE TABLE Orders (
    id STRING,
    id2 STRING,
    proc_time AS PROCTIME()
) WITH (
'connector' = 'datagen',
'rows-per-second' = '1',
'fields.id.kind' = 'sequence',
'fields.id.start' = '1',
'fields.id.end' = '120',
'fields.id2.kind' = 'sequence',
'fields.id2.start' = '2',
'fields.id2.end' = '120'
);

Using Customers table in Flink SQL Lookup Join with Orders table:

SELECT o.id, o.id2, c.msg, c.uuid, c.isActive, c.balance FROM Orders AS o 
JOIN Customers FOR SYSTEM_TIME AS OF o.proc_time AS c ON o.id = c.id AND o.id2 = c.id2

The columns and their values used for JOIN ON condition will be used as HTTP get parameters where the column name will be used as a request parameter name. For Example: http://localhost:8080/client/service?id=1&uuid=2

Http headers

It is possible to set HTTP headers that will be added to HTTP request send by lookup source connector. Headers are defined via property key gid.connector.http.source.lookup.header.HEADER_NAME = header value for example: gid.connector.http.source.lookup.header.X-Content-Type-Options = nosniff.

Headers can be set using http lookup source table DDL. In example below, HTTP request done for http-lookup table will contain three headers:

Origin
X-Content-Type-Options
Content-Type

CREATE TABLE http-lookup (
  id bigint,
  some_field string
) WITH (
  'connector' = 'rest-lookup',
  'format' = 'json',
  'url' = 'http://localhost:8080/client',
  'asyncPolling' = 'true',
  'gid.connector.http.source.lookup.header.Origin' = '*',
  'gid.connector.http.source.lookup.header.X-Content-Type-Options' = 'nosniff',
  'gid.connector.http.source.lookup.header.Content-Type' = 'application/json'
)

HTTP Sink

The following example shows the minimum Table API example to create a HttpDynamicSink that writes JSON values to an HTTP endpoint using POST method, assuming Flink has JAR of JSON serializer installed:

CREATE TABLE http (
  id bigint,
  some_field string
) WITH (
  'connector' = 'http-sink',
  'url' = 'http://example.com/myendpoint',
  'format' = 'json'
)

Then use INSERT SQL statement to send data to your HTTP endpoint:

INSERT INTO http VALUES (1, 'Ninette'), (2, 'Hedy')

Due to the fact that HttpSink sends bytes inside HTTP request's body, one can easily swap 'format' = 'json' for some other format.

Other examples of usage of the Table API can be found in some tests.

Http headers

It is possible to set HTTP headers that will be added to HTTP request send by sink connector. Headers are defined via property key gid.connector.http.sink.header.HEADER_NAME = header value for example: gid.connector.http.sink.header.X-Content-Type-Options = nosniff. Properties can be set via Sink builder or Property object:

HttpSink.<String>builder()
      .setEndpointUrl("http://example.com/myendpoint")
      .setElementConverter(
          (s, _context) -> new HttpSinkRequestEntry("POST", s.getBytes(StandardCharsets.UTF_8)))
      .setProperty("gid.connector.http.sink.header.X-Content-Type-Options", "nosniff")
      .build();

Properties properties = Properties();
properties.setProperty("gid.connector.http.sink.header.X-Content-Type-Options", "nosniff");

HttpSink.<String>builder()
      .setEndpointUrl("http://example.com/myendpoint")
      .setElementConverter(
          (s, _context) -> new HttpSinkRequestEntry("POST", s.getBytes(StandardCharsets.UTF_8)))
      .setProperties(properties)
      .build();

In Table/SQL API, headers can be set using http sink table DDL. In example below, HTTP request done for http table will contain three headers:

Origin
X-Content-Type-Options
Content-Type

CREATE TABLE http (
  id bigint,
  some_field string
) WITH (
  'connector' = 'http-sink',
  'url' = 'http://example.com/myendpoint',
  'format' = 'json',
  'gid.connector.http.sink.header.Origin' = '*',
  'gid.connector.http.sink.header.X-Content-Type-Options' = 'nosniff',
  'gid.connector.http.sink.header.Content-Type' = 'application/json'
)

HTTP status code handler (currently supported only for HTTP Sink)

Http Sink connector allows defining list of HTTP status codes that should be treated as errors. By default all 400s and 500s response codes will be interpreted as error code.

This behavior can be changed by using below properties in table definition (DDL) or passing it via `setProperty' method from Sink's builder. The property names are:

gid.connector.http.sink.error.code used to defined HTTP status code value that should be treated as error for example 404. Many status codes can be defined in one value, where each code should be separated with comma, for example: 401, 402, 403. User can use this property also to define a type code mask. In that case, all codes from given HTTP response type will be treated as errors. An example of such a mask would be 3XX, 4XX, 5XX. In this case, all 300s, 400s and 500s status codes will be treated as errors.
gid.connector.http.sink.error.code.exclude used to exclude a HTTP code from error list. Many status codes can be defined in one value, where each code should be separated with comma, for example: 401, 402, 403. In this example, codes 401, 402 and 403 would not be interpreted as error codes.

Table API Connector Options

HTTP TableLookup Source

Option	Required	Description/Value
connector	required	The Value should be set to rest-lookup
format	required	Flink's format name that should be used to decode REST response, Use `json` for a typical REST endpoint.
url	required	The base URL that should be use for GET requests. For example http://localhost:8080/client
asyncPolling	optional	true/false - determines whether Async Pooling should be used. Mechanism is based on Flink's Async I/O.

HTTP Sink

Option	Required	Description/Value
connector	required	Specify what connector to use. For HTTP Sink it should be set to 'http-sink'.
url	required	The base URL that should be use for HTTP requests. For example http://localhost:8080/client.
format	required	Specify what format to use.
insert-method	optional	Specify which HTTP method to use in the request. The value should be set either to `POST` or `PUT`.
sink.batch.max-size	optional	Maximum number of elements that may be passed in a batch to be written downstream.
sink.requests.max-inflight	optional	The maximum number of in flight requests that may exist, if any more in flight requests need to be initiated once the maximum has been reached, then it will be blocked until some have completed.
sink.requests.max-buffered	optional	Maximum number of buffered records before applying backpressure.
sink.flush-buffer.size	optional	The maximum size of a batch of entries that may be sent to the HTTP endpoint measured in bytes.
sink.flush-buffer.timeout	optional	Threshold time in milliseconds for an element to be in a buffer before being flushed.

Build and deployment

To build the project locally you need to have maven 3 and Java 11+.

Project build command: mvn package.
Detailed test report can be found under target/site/jacoco/index.xml.

Demo application

You can test this connector using simple mock http server provided with this repository and Flink SQL-client. The mock server can be started from IDE (currently only this way) by running HttpStubApp::main method. It will start HTTP server listening on http://localhost:8080/client

Steps to follow:

Run Mock HTTP server from HttpStubApp::main method.
Start your Flink cluster, for example as described under https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/try-flink/local_installation/
Start Flink SQL Client [6] by calling: ./bin/sql-client.sh -j flink-http-connector-1.0-SNAPSHOT.jar
Execute SQL statements: Create Data Stream source Table:

CREATE TABLE Orders (id STRING, id2 STRING, proc_time AS PROCTIME()
) WITH (
'connector' = 'datagen', 
'rows-per-second' = '1', 
'fields.id.kind' = 'sequence', 
'fields.id.start' = '1', 
'fields.id.end' = '120', 
'fields.id2.kind' = 'sequence', 
'fields.id2.start' = '2', 
'fields.id2.end' = '120'
);

Create Http Connector Lookup Table:

CREATE TABLE Customers (
	id STRING,
	id2 STRING,
	msg STRING,
	uuid STRING,
	details ROW<
	  isActive BOOLEAN,
	  nestedDetails ROW<
	    balance STRING
	  >
	>
) WITH (
'connector' = 'rest-lookup',
'format' = 'json' 
'url' = 'http://localhost:8080/client', 
'asyncPolling' = 'true'
);

Submit SQL Select query to join both tables:

SELECT o.id, o.id2, c.msg, c.uuid, c.isActive, c.balance FROM Orders AS o JOIN Customers FOR SYSTEM_TIME AS OF o.proc_time AS c ON o.id = c.id AND o.id2 = c.id2;

As a result, you should see a table with joined records like so:

The msg column shows parameters used with REST call for given JOIN record.

Implementation

HTTP Source

Implementation of an HTTP source connector is based on Flink's TableFunction and AsyncTableFunction classes.
To be more specific we are using a LookupTableSource. Unfortunately Flink's new unified source interface [2] cannot be used for this type of source. Issue was discussed on Flink's user mailing list - https://lists.apache.org/thread/tx2w1m15zt5qnvt924mmbvr7s8rlyjmw

Implementation of an HTTP Sink is based on Flink's AsyncSinkBase introduced in Flink 1.15 [3, 4].

Http Response to Table schema mapping

The mapping from Http Json Response to SQL table schema is done via Json Paths [5]. This is achieved thanks to com.jayway.jsonpath:json-path library.

If no root or field.#.path option is defined, the connector will use the column name as json path and will try to look for Json Node with that name in received Json. If no node with a given name is found, the connector will return null as value for this field.

If the field.#.path option is defined, connector will use given Json path from option's value in order to find Json data that should be used for this column. For example 'field.isActive.path' = '$.details.isActive' - the value for table column isActive will be taken from $.details.isActive node from received Json.

TODO

HTTP TableLookup Source

Implement caches.
Add support for other Flink types. Currently, STRING type is only fully supported.
Think about Retry Policy for Http Request
Use Flink Format [7] to parse Json response
Add Configurable Timeout value
Check other //TODO's.

HTTP Sink

Make HttpSink retry the failed requests. Currently, it does not retry those at all, only adds their count to the numRecordsSendErrors metric. It should be thoroughly thought over how to do it efficiently and then implemented.

[1] https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/dev/table/sql/queries/joins/#lookup-join
[2] https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/dev/datastream/sources/
[3] https://cwiki.apache.org/confluence/display/FLINK/FLIP-171%3A+Async+Sink
[4] https://nightlies.apache.org/flink/flink-docs-release-1.15/api/java/org/apache/flink/connector/base/sink/AsyncSinkBase.html
[5] https://support.smartbear.com/alertsite/docs/monitors/api/endpoint/jsonpath.html
[6] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sqlclient/
[7] https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/table/formats/json/

0xqq/flink-http-connector

flink-http-connector

Prerequisites

Runtime dependencies

Installation

Documentation

Usage

HTTP TableLookup Source

Http headers

HTTP Sink

Http headers

HTTP status code handler (currently supported only for HTTP Sink)

Table API Connector Options

HTTP TableLookup Source

HTTP Sink

Build and deployment

Demo application

Implementation

HTTP Source

Http Response to Table schema mapping

TODO

HTTP TableLookup Source

HTTP Sink