What is the Quix SDK?
The Quix SDK makes it quick and easy to develop streaming applications. It’s designed to be used for high performance telemetry services where you need to process high volumes of data in a nanosecond response time.
The SDK is available for Python and C#.
Using the Quix SDK, you can:
To support these operations, the SDK provides several useful features out of the box, and solves all the common problems you should face when developing real-time streaming applications:
The Quix SDK handles stream contexts for you, so all the data from one data source is bundled in the same scope. This allows you to attach metadata to streams.
The SDK simplifies the processing of streams by providing callbacks on the reading side. When processing stream data, you can identify data from different streams more easily than with the key-value approach used by other technologies.
Refer to the Streaming context section of this documentation for more information.
The Quix SDK is designed to make in-memory data processing extremely efficient. We use high-performance SDK features in conjunction with the message broker capabilities to achieve maximum throughput with the very minimum latency.
Refer to the In-memory data processing section of this documentation for more information.
If you’re sending data at high frequency, processing each message can be costly. The SDK provides a built-in buffers features for reading and writing to give you absolute freedom in balancing between latency and cost.
Refer to the Built-in buffers section of this documentation for more information.
In many use cases, multiple parameters are emitted at the same time, so they share one timestamp. Handling this data independently is wasteful. The SDK uses a rows system, and can work with Pandas DataFrames natively. Each row has a timestamp and user-defined tags as indexes.
Refer to the Support for Data Frames section of this documentation for more information.
The SDK automatically handles large messages on the producer side, splitting them up if required. You no longer need to worry about Kafka message limits. On the consumer side, those messages are automatically merged back.
Refer to the Message splitting section of this documentation for more information.
The Quix SDK automatically compresses your messages, reducing them by an average factor of 10 times. You save money via added efficiency.
The SDK also sends parameter values as the delta between timestamps, converting strings to flags, and in general reduces payload size for each message. This happens before compression is applied, so the final compression ratio is even higher.
Refer to the Message compression section of this documentation for more information.
The Quix SDK automatically serializes data from native types in your language. You can work with familiar data types, such as Pandas DataFrames, without worrying about conversion. Serialization can be painful, especially if it is done with performance in mind. We serialize native types using our codecs so you don’t have to worry about that.
Refer to the Data serialization section of this documentation for more information.
The SDK allows you to attach any type of data to your timestamps, like Numbers, Strings or even raw Binary data. This gives the SDK the ability to adapt to any streaming application use case.
Refer to the Multiple data types section of this documentation for more information.
Quix handles Kafka configuration efficiently and reliably. Our templates come with pre-configured certificates and connection settings. Many configuration settings are needed to use Kafka at its best, and the ideal configuration takes time! We take care of this in the SDK so you don’t have to.
Refer to the Broker configuration section of this documentation for more information.
The SDK allows you to do manual checkpointing when you read data from a Topic. This provides the ability to inform the Message Broker that you have already processed messages up to one point, usually called a checkpoint.
This is a very important concept when you are developing high performance streaming applications.
Refer to the Checkpointing section of this documentation for more information.
The Quix SDK provides horizontal scale out of the box via the streaming context feature. This means a data scientist or data engineer does not have to implement parallel processing themselves. You can scale the processing models, from one replica to many and back to one, and use the callback system inside the SDK to ensure that your data load is always shared between your model replicas.
Refer to the Horizontal scaling section of this documentation for more information.
The SDK offers integrations out of the box, including data persistence and historic or real-time APIs with other systems. That means you don’t have to implement them by yourself.
Refer to the Integrations section of this documentation for more information.
The Quix SDK is an abstraction layer over a concrete broker technology. You’re not locked into a specific broker and can innovate over time.
Refer to the Portability section of this documentation for more information.
The Quix SDK uses an internal protocol which is both data and speed optimized so we do encourage you to use it. For that you need to use the SDK on both producer ( writer ) and consumer ( reader ) sides.
However, in some cases, you simply do not have the ability to run the Quix SDK on both sides.