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Developing
Develop stream applications with the Streams Processing Language (SPL), Java, and Python.
Writing stream applications
Teracloud® Streams provides features and functions to help you write your applications to fulfill your business needs.
Developing a simple application
Companies build stream applications when they need to extract information from large data sets so that they can analyze only the information that is relevant to them.

Welcome
Learn about the core capabilities of Teracloud® Streams, its architecture, and key concepts.
Installing
Use this information to install, upgrade, and uninstall the Teracloud® Streams product.
Configuring
Create a basic or an enterprise domain which is a single point for configuring and managing common resources, security, and instances.
Administering
Administer the product by using the Teracloud® Streams graphical user interface, APIs, or the streamtool command-line interface.
Developing
Develop stream applications with the Streams Processing Language (SPL), Java, and Python.
- Development concepts
  Development of stream applications consists of several components such as operators, streams, tuples, Streams Processing Language, toolkits, and more.
- Writing stream applications
  Teracloud® Streams provides features and functions to help you write your applications to fulfill your business needs.
  - Streams Processing Language
    Streams Processing Language (SPL) is a distributed data flow composition language. SPL has primitive types, program structures, and definitions that are tailored for streaming data.
  - Developing a simple application
    Companies build stream applications when they need to extract information from large data sets so that they can analyze only the information that is relevant to them.
    - Reading data from a source file
      Suppose that the stock transaction data for a day contains the following sample data in a file named StockTrades.csv.
    - Filtering large data sets
      In this example, the stream application needs to filter the stock transaction data for IBM transaction records. You use the Filter operator to extract relevant information from potentially large volumes of data. As shown, the input for the Filter operator is all the transactions; the output is only the IBM transactions.
    - Analyzing the subset of data
      To determine the highest and lowest trading price, you need to write the logic for determining those values. In addition, you need some local variables to track the highest and lowest price that is processed so far. You can use the Custom operator to accomplish these tasks. As shown, the input stream is only the IBM transactions; there is no output stream for the customized operator.
    - Combining the operators into a graph
      A stream application that is written in SPL code is a directed graph where streams of data (edges in the graph) flow into and out of operators (nodes in the graph). Operators perform analysis on the incoming data; and produce new streams of data which then flow into subsequent operators. You can use this basic pattern, input data --> operator --> output data, to create an assembly line of analytics where downstream operators use data from upstream operators to perform analytics and produce new data.
    - Testing the application
      Test the stream application to verify that the code generated from the graph is correct. You must build (compile) the application before you can run it. You can run the application in stand-alone mode or in a distributed environment..
  - Developing another simple application
    In addition to filtering large data sets, you can reduce the volume of data by reducing the size of the tuples from the input stream.
  - Best practices for developing applications
    Use these tips to write operators that perform effectively in their application, and in other applications.
  - Developing applications with user-defined parallelism
    The @parallel annotation allows developers to easily take advantage of data-parallelism. In the streaming context, data-parallelism means replicating copies of operators, and splitting streams so that different tuples go to each set of replicas. The process of replicating operators, and creating all of the new streams to connect them, is called the parallel transformation. When developers add @parallel to a primitive or composite operator invocation, Teracloud Streams will perform the parallel transformation at submission time. The goal is to improve overall application throughput by executing the replicas in parallel.
  - Developing applications with consistent regions
    Because of business requirements, some applications require that all tuples in an application are processed at least once. You can use a consistent region in your stream applications to avoid data loss due to software or hardware failure and meet your requirements for at-least-once processing.
  - Developing applications with views
    To make streaming data available to external programs, your SPL application must create at least one view. A view defines the set of attributes that can appear in a specific viewable data stream.
  - Developing applications with event time
    Event time is a simple model which supports streams processing where time is not derived from the system time of the machine Teracloud® Streams is running on, but from a time value associated with each tuple. In a graph enabled for event time, tuples have an attribute which holds their time value. The time value for each tuple enables operations such as grouping tuples with a time value falling within specified time intervals, and running aggregate calculations on the group.
  - Logging and tracing options for SPL application developers
    SPL application developers can write messages to product log and trace files. Log and trace files help administrators, instance owners, application developers, and the support team to identify, diagnose, and resolve problems with the Teracloud® Streams and applications.
- Enabling Streams data exchange
  Teracloud® Streams provides a data exchange REST API for inserting and retrieving Streams job data. You can add one or more endpoint operators to your Streams application to enable Streams data exchange. Providing a standard REST interface for Streams data exchange eases the integration of Streams data with other data services and externally hosted applications.
- Compiling stream applications
  The Streams Processing Language (SPL) Compiler is called sc and is included with the Teracloud® Streams installation package. You compile SPL to create files to run on your Teracloud Streams instance.
- Performance improvements for stream applications
  You can improve the processing of your stream application by following these guidelines and tips.
- Debugging stream applications
  The Streams Processing Language (SPL) Streams Debugger (sdb) provides a command line-based interactive debugger for debugging stream applications.
- Developing toolkits and operators
  You can create custom toolkits and operators for your applications.
Troubleshooting
Resolve problems with Teracloud® Streams using the troubleshooting tools provided with the product as well as the resources offered by Teracloud Support.
Reference
Find details on the SPL language, toolkits, APIs, commands, and more.
Glossary
Use this glossary to find terms and definitions for Teracloud® Streams.

Developing a simple application

Companies build stream applications when they need to extract information from large data sets so that they can analyze only the information that is relevant to them.

About this task

Suppose you wanted to know the highest and lowest trading price for IBM® stocks for a specific day. To determine this, you can write a stream application to filter the stock transaction data to process only the IBM® transaction records, and then process the IBM® transaction records to find the highest and lowest trading price for that day.

Developing a simple application

About this task

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