---
title: "DataStorm"
url: https://docs.zeroc.com/ice/3.8/services/datastorm
docs_index: https://docs.zeroc.com/llms.txt
---

> For an index of every page, see [llms.txt](https://docs.zeroc.com/llms.txt).

# DataStorm

**DataStorm** is a **brokerless, C++ publish–subscribe framework**.

DataStorm helps you exchange data between your networked applications, with a particular focus on:

- Ease of use - simple exchanges require just a few lines of code
- Flexibility
- High performance

A DataStorm application consists of *publishers* and *subscribers*. Publishers write data to **topics**, and subscribers read data from these topics.

A **topic** is a named object within a DataStorm node. It can be seen as a stream of typed data. The unit of data exchanged between topics is called a **sample**. A sample consists of a key–value pair (or data element) along with additional metadata attached when it is written.

A typical DataStorm application consists of multiple **nodes**. Within these nodes, you create topics; each topic represents a type of data you want to distribute. For each topic, you create **writers** and **readers** to produce and consume the samples that flow between nodes.

![A temperature writer publishes through its topic and Node A. Samples travel to Node B, then through its temperature topic to a reader.](https://docs.zeroc.com/images/ice/3.8/datastorm-overview/datastorm-overview.svg)

DataStorm applications do not rely on a central service to communicate. Nodes can connect directly to each other and exchange samples whenever they share common topics for which one node has a writer and another has a matching reader.

Nodes can be statically configured to connect to each other (by providing endpoints), or they can use **UDP multicast** for automatic peer discovery. When using **TCP** for discovery, nodes can connect to other regular or dedicated discovery nodes. Discovery nodes can also be replicated and connected together to ensure there is no single point of failure.

## Filtering and Updates

DataStorm provides flexible mechanisms to control which samples are exchanged between nodes.

- **Key filters** allow readers to subscribe only to specific data elements within a topic.
- **Sample filters** allow readers to receive only samples that match a given condition.

Filters are application-defined functions, and DataStorm includes several predefined ones, such as a regular expression filter for string keys.

To minimize bandwidth usage, DataStorm supports **partial updates**, allowing writers to send only the portions of a value that have changed.

## Relationship to Ice

DataStorm is built on top of Ice but can also be used independently. You don’t need to know the Ice APIs to develop DataStorm applications—you only need to use the DataStorm API.

DataStorm is **encoding-agnostic**, meaning the format of data samples is entirely determined by the application. You are free to choose the encoding that best fits your use case.

For data types defined in **Slice**, DataStorm automatically uses the generated Slice-to-C++ code for serialization and deserialization. For other data encodings, you can provide your own `encode` and `decode` functions.

## DataStorm vs. IceStorm

Ice also includes [**IceStorm**](https://docs.zeroc.com/ice/3.8/services/icestorm.md), a broker-based publish/subscribe service that distributes Ice invocations to subscribers. In contrast, **DataStorm** is a brokerless, data-centric framework focused on efficiently distributing *data samples* rather than remote calls.

Use **IceStorm** when your application revolves around Ice interfaces and operations. Use **DataStorm** when you want lightweight, high-performance data sharing without a central broker.