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kafka-connect-http

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Kafka Connect connector that enables Change Data Capture from JSON/HTTP APIs into Kafka.

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# Kafka Connect HTTP Connector
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Kafka Connect connector that enables [Change Data Capture](docs/Change_Data_Capture.md) from JSON/HTTP APIs into Kafka.

## This connector is for you if
*   You want to (live) replicate a dataset exposed through JSON/HTTP API
*   You want to do so efficiently
*   You want to capture only changes, not full snapshots
*   You want to do so via configuration, with no custom coding
*   You want to be able to extend the connector if it comes to that

### Examples

See [examples](examples), e.g. 
*   [Jira Issues Search API](examples/jira-issues-search.md)
*   [Elasticsearch Search API](examples/elasticsearch-search.md)

## Getting Started

If your Kafka Connect deployment is automated and packaged with Maven, you can unpack the artifact on Kafka Connect
plugins folder.
```xml
<plugin>
    <artifactId>maven-dependency-plugin</artifactId>
    <execution>
        <id>copy-kafka-connect-plugins</id>
        <phase>prepare-package</phase>
        <goals>
            <goal>unpack</goal>
        </goals>
        <configuration>
            <outputDirectory>${project.build.directory}/docker-build/plugins</outputDirectory>
            <artifactItems>
                <artifactItem>
                    <groupId>com.github.castorm</groupId>
                    <artifactId>kafka-connect-http</artifactId>
                    <version>0.8.11</version>
                    <type>tar.gz</type>
                    <classifier>plugin</classifier>
                </artifactItem>
            </artifactItems>
        </configuration>
    </execution>
</plugin>
```
Otherwise, you'll have to do it manually by downloading the package from the
[Releases Page](https://github.com/castorm/kafka-connect-http/releases).

More details on how to [Install Connectors](https://docs.confluent.io/current/connect/managing/install.html).

## Source Connector
`com.github.castorm.kafka.connect.http.HttpSourceConnector`

### Extension points
The connector can be easily extended by implementing your own version of any of the components below.

These are better understood by looking at the source task implementation:
```java
public List<SourceRecord> poll() throws InterruptedException {

throttler.throttle(offset.getTimestamp().orElseGet(Instant::now));

HttpRequest request = requestFactory.createRequest(offset);

HttpResponse response = requestExecutor.execute(request);

List<SourceRecord> records = responseParser.parse(response);

List<SourceRecord> unseenRecords = recordSorter.sort(records).stream()
            .filter(recordFilterFactory.create(offset))
            .collect(toList());

confirmationWindow = new ConfirmationWindow<>(extractOffsets(unseenRecords));

return unseenRecords;
}

public void commitRecord(SourceRecord record, RecordMetadata metadata) {
    confirmationWindow.confirm(record.sourceOffset());
}

public void commit() {
    offset = confirmationWindow.getLowWatermarkOffset()
            .map(Offset::of)
            .orElse(offset);
}
```

---
<a name="timer"/>

### `Timer`: Throttling `HttpRequest`
Controls the rate at which HTTP requests are performed by informing the task, how long until the next execution is due.

> #### `http.timer`
> ```java
> public interface Timer extends Configurable {
> 
>     Long getRemainingMillis();
> 
>     default void reset(Instant lastZero) {
>         // Do nothing
>     }
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.timer.AdaptableIntervalTimer`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.timer.FixedIntervalTimer`
>     *   `com.github.castorm.kafka.connect.timer.AdaptableIntervalTimer`

#### Throttling `HttpRequest` with `FixedIntervalThrottler`

Throttles rate of requests based on a fixed interval.

> ##### `http.timer.interval.millis`
> Interval in between requests
> *   Type: `Long`
> *   Default: `60000`

#### Throttling HttpRequests with `AdaptableIntervalThrottler`

Throttles rate of requests based on a fixed interval.
It has, however, two modes of operation, with two different  intervals:
*   **Up to date** No new records in last poll, or there were new records, but "recently" created (shorter than interval) 
*   **Catching up** There were new records in last poll, but they were created "long ago" (longer than interval)

> ##### `http.timer.interval.millis`
> Interval in between requests when up-to-date
> 
> *   Type: `Long`
> *   Default: `60000`
> 
> ##### `http.timer.catchup.interval.millis`
> Interval in between requests when catching up
> *   Type: `Long`
> *   Default: `30000`

---
<a name="request"/>

### `HttpRequestFactory`: Creating a `HttpRequest`
The first thing our connector will need to do is creating a `HttpRequest`.

> #### `http.request.factory`
> ```java
> public interface HttpRequestFactory extends Configurable {
> 
>     HttpRequest createRequest(Offset offset);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.request.template.TemplateHttpRequestFactory`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.request.template.TemplateHttpRequestFactory`
>
> #### `http.offset.initial`
> Initial offset, comma separated list of pairs.
> *   Example: `property1=value1, property2=value2`
> *   Type: `String`
> *   Default: `""`

#### Creating a `HttpRequest` with `TemplateHttpRequestFactory`
This `HttpRequestFactory` is based on template resolution.

> ##### `http.request.method`
> Http method to use in the request.
> *   Type: `String`
> *   Default: `GET`
> 
> ##### `http.request.url`
> Http url to use in the request.
> *   __Required__
> *   Type: `String`
> 
> ##### `http.request.headers`
> Http headers to use in the request, `,` separated list of `:` separated pairs.
> *   Example: `Name: Value, Name2: Value2`
> *   Type: `String`
> *   Default: `""`
> 
> ##### `http.request.params`
> Http query parameters to use in the request, `&` separated list of `=` separated pairs.
> *   Example: `name=value & name2=value2`
> *   Type: `String`
> *   Default: `""`
> 
> ##### `http.request.body`
> Http body to use in the request.
> *   Type: `String`
> *   Default: `""`
> 
> ##### `http.request.template.factory`
> ```java
> public interface TemplateFactory {
> 
>     Template create(String template);
> }
> 
> public interface Template {
> 
>     String apply(Offset offset);
> }
> ```
> Class responsible for creating the templates that will be used on every request.
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.request.template.freemarker.BackwardsCompatibleFreeMarkerTemplateFactory`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.request.template.freemarker.BackwardsCompatibleFreeMarkerTemplateFactory`
          Implementation based on [FreeMarker](https://freemarker.apache.org/) which accepts offset properties without 
          `offset` namespace _(Deprecated)_
>     *   `com.github.castorm.kafka.connect.http.request.template.freemarker.FreeMarkerTemplateFactory`
          Implementation based on [FreeMarker](https://freemarker.apache.org/)
>     *   `com.github.castorm.kafka.connect.http.request.template.NoTemplateFactory`

##### Creating a `HttpRequest` with `FreeMarkerTemplateFactory`
FreeMarker templates will have the following data model available:
*   `offset`
    *   `key`
    *   `timestamp` (as ISO8601 string, e.g.: `2020-01-01T00:00:00Z`)
    *   ... _(custom offset properties)_

Accessing any of the above withing a template can be achieved like this:
```properties
http.request.params=after=${offset.timestamp}
```
For an Epoch representation of the same string, FreeMarker built-ins should be used:
```properties
http.request.params=after=${offset.timestamp?datetime.iso?long}
```
For a complete understanding of the features provided by FreeMarker, please, refer to the 
[User Manual](https://freemarker.apache.org/docs/index.html)

---
<a name="client"/>

### `HttpClient`: Executing a `HttpRequest`
Once our HttpRequest is ready, we have to execute it to get some results out of it. That's the purpose of the 
`HttpClient`

> #### `http.client`
> ```java
> public interface HttpClient extends Configurable {
> 
>     HttpResponse execute(HttpRequest request) throws IOException;
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.client.okhttp.OkHttpClient`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.client.okhttp.OkHttpClient`

#### Executing a `HttpRequest` with `OkHttpClient`
Uses a [OkHttp](https://square.github.io/okhttp/) client.

> ##### `http.client.connection.timeout.millis`
> Timeout for opening a connection
> *   Type: `Long`
> *   Default: `2000`
> 
> ##### `http.client.read.timeout.millis`
> Timeout for reading a response
> *   Type: `Long`
> *   Default: `2000`
> 
> ##### `http.client.connection.ttl.millis`
> Time to live for the connection
> *   Type: `Long`
> *   Default: `300000`
> 
> ##### `http.client.proxy.host`
> Hostname of the HTTP Proxy
> *   Type: `String`
> *   Default: ``
> 
> ##### `http.client.proxy.port`
> Port of the HTTP Proxy
> *   Type: `Integer`
> *   Default: `3128`
> 
> ##### `http.client.proxy.username`
> Username of the HTTP Proxy
> *   Type: `String`
> *   Default: ``
> 
> ##### `http.client.proxy.password`
> Password of the HTTP Proxy
> *   Type: `String`
> *   Default: ``
---
<a name="auth"/>

### `HttpAuthenticator`: Authenticating a `HttpRequest`
When executing the request, authentication might be required. The `HttpAuthenticator` is responsible for resolving the `Authorization` header
to be included in the `HttpRequest`.

> #### `http.auth`
> ```java
> public interface HttpAuthenticator extends Configurable {
> 
>     Optional<String> getAuthorizationHeader();
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.auth.ConfigurableHttpAuthenticator`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.auth.ConfigurableHttpAuthenticator`
>     *   `com.github.castorm.kafka.connect.http.auth.NoneHttpAuthenticator`
>     *   `com.github.castorm.kafka.connect.http.auth.BasicHttpAuthenticator`

#### Authenticating with `ConfigurableHttpAuthenticator`
Allows selecting the authentication type via configuration property

> ##### `http.auth.type`
> Type of authentication
> *   Type: `Enum { None, Basic }`
> *   Default: `None`

#### Authenticating with `BasicHttpAuthenticator`
Allows selecting the authentication type via configuration property

> ##### `http.auth.user`
> *   Type: `String`
> *   Default: `""`
>
> ##### `http.auth.password`
> *   Type: `String`
> *   Default: `"""`

---
<a name="response"/>

### `HttpResponseParser`: Parsing a `HttpResponse`
Once our `HttpRequest` has been executed, as a result we'll have to deal with a `HttpResponse` and translate it into 
the list of `SourceRecord`s expected by Kafka Connect.

> #### `http.response.parser`
> ```java
> public interface HttpResponseParser extends Configurable {
> 
>     List<SourceRecord> parse(HttpResponse response);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.response.PolicyHttpResponseParser`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.response.PolicyHttpResponseParser`
>     *   `com.github.castorm.kafka.connect.http.response.KvHttpResponseParser`

#### Parsing with `PolicyHttpResponseParser`
Vets the HTTP response deciding whether the response should be processed, skipped or failed. This decision is delegated
to a `HttpResponsePolicy`. 
When the decision is to process the response, this processing is delegated to a secondary `HttpResponseParser`.

##### `HttpResponsePolicy`: Vetting a `HttpResponse`

> ##### `http.response.policy`
> ```java
> public interface HttpResponsePolicy extends Configurable {
> 
>     HttpResponseOutcome resolve(HttpResponse response);
> 
>     enum HttpResponseOutcome {
>         PROCESS, SKIP, FAIL
>     }
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.response.StatusCodeHttpResponsePolicy`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.response.StatusCodeHttpResponsePolicy`
>
> ##### `http.response.policy.parser`
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.response.KvHttpResponseParser`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.response.KvHttpResponseParser`

###### Vetting with `StatusCodeHttpResponsePolicy`
Does response vetting based on HTTP status codes in the response and the configuration below.

> ##### `http.response.policy.codes.process`
> Comma separated list of code ranges that will result in the parser processing the response
> *   Example: `200..205, 207..210`
> *   Type: `String`
> *   Default: `200..299`
>
> ##### `http.response.policy.codes.skip`
> Comma separated list of code ranges that will result in the parser skipping the response
> *   Example: `300..305, 307..310`
> *   Type: `String`
> *   Default: `300..399`

#### Parsing with `KvHttpResponseParser`
Parses the HTTP response into a key-value SourceRecord. This process is decomposed in two steps:
*   Parsing the `HttpResponse` into a `KvRecord`
*   Mapping the `KvRecord` into a `SourceRecord`

> ##### `http.response.record.parser`
> ```java
> public interface KvRecordHttpResponseParser extends Configurable {
> 
>     List<KvRecord> parse(HttpResponse response);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.response.jackson.JacksonKvRecordHttpResponseParser`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.response.jackson.JacksonKvRecordHttpResponseParser`
>
> ##### `http.response.record.mapper`
> ```java
> public interface KvSourceRecordMapper extends Configurable {
> 
>     SourceRecord map(KvRecord record);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.record.SchemedKvSourceRecordMapper`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.record.SchemedKvSourceRecordMapper`
>         Maps __key__ to a *Struct schema* with a single property `key`, and __value__ to a *Struct schema* with a single property `value`
>     *   `com.github.castorm.kafka.connect.http.record.StringKvSourceRecordMapper`
>         Maps both __key__ and __value__ to a `String` schema

##### Parsing with `JacksonKvRecordHttpResponseParser`
Uses [Jackson](https://github.com/FasterXML/jackson) to look for the records in the response.

> ##### `http.response.list.pointer`
> [JsonPointer](https://tools.ietf.org/html/rfc6901) to the property in the response body containing an array of records 
> *   Example: `/items`
> *   Type: `String`
> *   Default: `/`
> 
> ##### `http.response.record.pointer`
> [JsonPointer](https://tools.ietf.org/html/rfc6901) to the individual record to be used as kafka record body. Useful
  when the object we are interested in is under a nested structure
> *   Type: `String`
> *   Default: `/`
>
> ##### `http.response.record.offset.pointer`
> Comma separated list of `key=/value` pairs where the key is the name of the property in the offset, and the value is
> the [JsonPointer](https://tools.ietf.org/html/rfc6901) to the value being used as offset for future requests.
> This is the mechanism that enables sharing state in between `HttpRequests`. `HttpRequestFactory` implementations 
> receive this `Offset`.
>
> Special properties:
> - `key` is used as record's identifier, used for *de-duplication* and topic *partition routing*
> - `timestamp` is used as record's timestamp, used for *de-duplication* and *ordering*
> 
> One of the roles of the offset, even if not required for preparing the next request, is helping in deduplication of
> already seen records, by providing a sense of progress, assuming consistent ordering. (e.g. even if the response returns
> some repeated results in between requests because they have the same timestamp, anything prior to the last seen
> offset will be ignored). see `OffsetFilterFactory`
> *   Example: `id=/itemId`
> *   Type: `String`
> *   Default: `""`
>
> ##### `http.response.record.timestamp.parser`
> Class responsible for converting the timestamp property captured above into a `java.time.Instant`.  
> *   Type: `String`
> *   Default: `com.github.castorm.kafka.connect.http.response.timestamp.EpochMillisOrDelegateTimestampParser`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.response.timestamp.EpochMillisTimestampParser` 
           Implementation that captures the timestamp as an epoch millis long
>     *   `com.github.castorm.kafka.connect.http.response.timestamp.EpochMillisOrDelegateTimestampParser` 
           Implementation that tries to capture as epoch millis or delegates to another parser in case of failure
>     *   `com.github.castorm.kafka.connect.http.response.timestamp.DateTimeFormatterTimestampParser` 
           Implementation based on based on a `DateTimeFormatter`
>     *   `com.github.castorm.kafka.connect.http.response.timestamp.NattyTimestampParser`
           Implementation based on [Natty](http://natty.joestelmach.com/) parser
>     *   `com.github.castorm.kafka.connect.http.response.timestamp.RegexTimestampParser`
          Implementation that extracts substring from timestamp column and parse it

> ##### `http.response.record.timestamp.parser.pattern`
> When using `DateTimeFormatterTimestampParser`, a custom pattern can be specified 
> *   Type: `String`
> *   Default: `yyyy-MM-dd'T'HH:mm:ss[.SSS]X`
> 
> ##### `http.response.record.timestamp.parser.zone`
> Timezone of the timestamp. Accepts [ZoneId](https://docs.oracle.com/javase/8/docs/api/java/time/ZoneId.html) valid
  identifiers
> *   Type: `String`
> *   Default: `UTC`
>
> ##### `http.response.record.timestamp.parser.regex`
> When using `RegexTimestampParser`, a custom regex pattern can be specified 
> *   Type: `String`
> *   Default: `.*`
>
##### `http.response.record.timestamp.parser.regex.delegate`
> When using `RegexTimestampParser`, a delegate class to parse timestamp 
> *   Type: `Class`
> *   Default: `DateTimeFormatterTimestampParser`

---
<a name="mapper"/>

### Mapping a `KvRecord` into `SourceRecord` with `SimpleKvSourceRecordMapper`
Once we have our `KvRecord` we have to translate it into what Kafka Connect is expecting: `SourceRecord`s

Embeds the record properties into a common simple envelope to enable schema evolution. This envelope simply contains
a key and a value properties with customizable field names.

Here is also where we'll tell Kafka Connect to what topic and on what partition do we want to send our record.

** It's worth noticing there are projects out there that allow you to infer the schema from your json document.
(e.g. [expandjsonsmt](https://github.com/RedHatInsights/expandjsonsmt))

> ##### `kafka.topic`
> Name of the topic where the record will be sent to
> *   __Required__
> *   Type: `String`
> *   Default: `""`
>
> ##### `http.record.schema.key.property.name`
> Name of the key property in the key-value envelope
> *   Type: `String`
> *   Default: `key`
>
> ##### `http.record.schema.value.property.name`
> Name of the value property in the key-value envelope
> *   Type: `String`
> *   Default: `value`

---
<a name="sorter"/>

### SourceRecordSorter: Sorting SourceRecords
Some Http resources not designed for CDC, return snapshots with most recent records first. In this cases de-duplication
is especially important, as subsequent request are likely to produce similar results. The de-duplication mechanisms 
offered by this connector are order-dependent, as they are usually based on timestamps.

To enable de-duplication in cases like this, we can instruct the connector to assume a specific order direction, either
`ASC`, `DESC`, or `IMPLICIT`, where implicit figures it out based on records' timestamps.

> #### `http.record.sorter`
> ```java
> public interface SourceRecordSorter extends Configurable {
> 
>     List<SourceRecord> sort(List<SourceRecord> records);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.record.OrderDirectionSourceRecordSorter`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.record.OrderDirectionSourceRecordSorter`
>
> #### `http.response.list.order.direction`
> Order direction of the results in the response list.
> *   Type: `Enum { ASC, DESC, IMPLICIT }`
> *   Default: `IMPLICIT`

---
<a name="filter"/>

### SourceRecordFilterFactory: Filtering out SourceRecord
There are cases when we'll be interested in filtering out certain records. One of these would be de-duplication.

> #### `http.record.filter.factory`
> ```java
> public interface SourceRecordFilterFactory extends Configurable {
> 
>     Predicate<SourceRecord> create(Offset offset);
> }
> ```
> *   Type: `Class`
> *   Default: `com.github.castorm.kafka.connect.http.record.OffsetRecordFilterFactory`
> *   Available implementations:
>     *   `com.github.castorm.kafka.connect.http.record.OffsetRecordFilterFactory`
>     *   `com.github.castorm.kafka.connect.http.record.OffsetTimestampRecordFilterFactory`
>     *   `com.github.castorm.kafka.connect.http.record.PassthroughRecordFilterFactory`

#### Filtering out SourceRecord with OffsetTimestampRecordFilterFactory

De-duplicates based on `Offset`'s timestamp, filtering out records with earlier or the same timestamp. 
Useful when timestamp is used to filter the HTTP resource, but the filter does not have full timestamp precision.
Assumptions:
*   Records are ordered by timestamp
*   No two records can contain the same timestamp (to whatever precision the HTTP resource uses)

If the latter assumption cannot be satisfied, check `OffsetRecordFilterFactory` to try and prevents data loss.

#### Filtering out SourceRecord with OffsetRecordFilterFactory

De-duplicates based on `Offset`'s timestamp, key and any other custom property present in the `Offset`, filtering out 
records with earlier timestamps, or when in the same timestamp, only those up to the last seen `Offset` properties.
Useful when timestamp alone is not unique but together with some other `Offset` property is.
Assumptions: 
*   Records are ordered by timestamp
*   There is an `Offset` property that uniquely identify records (e.g. key)
*   There won't be new items preceding already seen ones

---
## Development

### Building
```bash
mvn package
```
### Debugging

#### Using Pre-configured docker setup

You can easily run a Kafka Connect cluster with kafka-connect-http pre-installed by executing:
```bash
mvn verify -Pdebug -DskipTests
```
It'll run dockerized versions of kafka and kafka-connect which you can access via REST API or attach debuggers to the url printed in
console:
```bash
Kafka Connect testcontainers infra is ready
  Rest API: http://localhost:33216
  Debug agent: localhost:33217
```
Right after, it'll allow you to specify the file path to your connector's json configuration:
```bash:
Introduce the path to your connector JSON configuration file:
```
It'll subscribe to the corresponding kafka topic, printing every message going through the output topic of your connector.

#### Using Kafka Connect standalone
_These instructions are phrased in terms of the steps needed when using IntelliJ,
but other integrated development environments are likely to be similar._

Point the Kafka stand-alone **plugin.path** at the module compile **Output path**.
Assuming you are using the default Maven project import,
this is the **./target** directory, so the **config/connect-standalone.properties** file would contain the line
```bash
plugin.path=<directory where git clone was executed>/kafka-connect-http/kafka-connect-http/target
```
In the **Run/Debug Configurations** dialog, create a new **Remote JVM Debug** configuration with the mode **Attach to remote JVM**.
When remote debugging, some Java parameters need to be specified when the program is executed.
Fortunately there are hooks in the Kafka shell scripts to accommodate this.
The **Remote JVM Debug** configuration specifies the needed **Command line arguments for remote JVM**.
In the terminal console where you execute the connect command line, define **KAFKA_DEBUG** and **JAVA_DEBUG_OPTS** as:
```bash
export KAFKA_DEBUG=true
export JAVA_DEBUG_OPTS=-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=*:5005
```
Place a suitable breakpoint in the kafka-connect-http code, e.g. in `HttpSourceTask.start()`, and launch the standalone connect program:
```bash
bin/connect-standalone.sh config/connect-standalone.properties plugins/<kafka-connect-http properties file>
```
Click the Debug icon in IntelliJ and ensure
the debugger console says `Connected to the target VM, address: 'localhost:5005', transport: 'socket'`
and the breakpoint you placed becomes checked.
The program should now break when the breakpoint is hit.
### Running the tests
```bash
mvn test
```
### Releasing
*   Update [CHANGELOG.md](CHANGELOG.md) and [README.md](README.md) files.
*   Prepare release:
```bash
mvn release:clean release:prepare
```

## Contributing

Contributions are welcome via pull requests, pending definition of code of conduct, please just follow existing conventions.

## Versioning

We use [SemVer](http://semver.org/) for versioning.

## License

This project is licensed under the Apache 2.0 License - see the [LICENSE.txt](LICENSE.txt) file for details

## Built With

*   [Maven](https://maven.apache.org/) - Dependency Management
*   [Kafka Connect](https://kafka.apache.org/documentation/#connect) - The framework for our connectors
*   [OkHttp](https://square.github.io/okhttp/) - HTTP Client
*   [Jackson](https://github.com/FasterXML/jackson) - Json deserialization
*   [FreeMarker](https://freemarker.apache.org/) - Template engine
*   [Natty](http://natty.joestelmach.com/) - Date parser

## Acknowledgments

*   Inspired by [llofberg/kafka-connect-rest](https://github.com/llofberg/kafka-connect-rest)

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