id: version-0.20.0-heron-topology-concepts title: Heron Topologies sidebar_label: Heron Topologies original_id: heron-topology-concepts

Don't want to manually create spouts and bolts? Try the Heron Streamlet API.. If you find manually creating and connecting spouts and bolts to be overly cumbersome, we recommend trying out the Heron Streamlet API for Java, which enables you to create your topology logic using a highly streamlined logic inspired by functional programming concepts.

A Heron topology is a directed acyclic graph (DAG) used to process streams of data. Topologies can be stateless or stateful depending on your use case.

Heron topologies consist of two basic components:

  • Spouts inject data into Heron topologies, potentially from external sources like pub-sub messaging systems (Apache Kafka, Apache Pulsar, etc.)
  • Bolts apply user-defined processing logic to data supplied by spouts

Spouts and bolts are connected to one another via streams of data. Below is a visual illustration of a simple Heron topology:

Heron topology

In the diagram above, spout S1 feeds data to bolts B1 and B2 for processing; in turn, bolt B1 feeds processed data to bolts B3 and B4, while bolt B2 feeds processed data to bolt B4. This is just a simple example; you can create arbitrarily complex topologies in Heron.

Creating topologies

There are currently two APIs available that you can use to build Heron topologies:

  1. The higher-level Heron Streamlet API, which enables you to create topologies in a declarative, developer-friendly style inspired by functional programming concepts (such as map, flatMap, and filter operations)
  2. The lower-level topology API , based on the original Apache Storm API, which requires you to specify spout and bolt logic directly

Topology Lifecycle

Once you‘ve set up a Heron cluster, you can use Heron’s CLI tool to manage the entire lifecycle of a topology, which typically goes through the following stages:

  1. Submit the topology to the cluster. The topology is not yet processing streams but is ready to be activated.
  2. Activate the topology. The topology will begin processing streams in accordance with the topology architecture that you've created.
  3. Restart an active topology if, for example, you need to update the topology configuration.
  4. Deactivate the topology. Once deactivated, the topology will stop processing but remain running in the cluster.
  5. Kill a topology to completely remove it from the cluster. It is no longer known to the Heron cluster and can no longer be activated. Once killed, the only way to run that topology is to re-submit and re-activate it.

Logical Plan

A topology‘s logical plan is analagous to a database query plan in that it maps out the basic operations associated with a topology. Here’s an example logical plan for the example Streamlet API topology below:

Topology logical Plan

Whether you use the Heron Streamlet API or the topology API, Heron automatically transforms the processing logic that you create into both a logical plan and a physical plan.

Physical Plan

A topology‘s physical plan is related to its logical plan but with the crucial difference that a physical plan determines the “physical” execution logic of a topology, i.e. how topology processes are divided between containers. Here’s a basic visual representation of a physical plan:

Topology Physical Plan

In this example, a Heron topology consists of two spout and five different bolts (each of which has multiple instances) that have automatically been distributed between five different containers.

Window operations

Windowed computations gather results from a topology or topology component within a specified finite time frame rather than, say, on a per-tuple basis.

Here are some examples of window operations:

  • Counting how many customers have purchased a product during each one-hour period in the last 24 hours.
  • Determining which player in an online game has the highest score within the last 1000 computations.

Sliding windows

Sliding windows are windows that overlap, as in this figure:

Sliding time window

For sliding windows, you need to specify two things:

  1. The length or duration of the window (length if the window is a count window, duration if the window is a time window).
  2. The sliding interval, which determines when the window slides, i.e. at what point during the current window the new window begins.

In the figure above, the duration of the window is 20 seconds, while the sliding interval is 10 seconds. Each new window begins five seconds into the current window.

With sliding time windows, data can be processed in more than one window. Tuples 3, 4, and 5 above are processed in both window 1 and window 2 while tuples 6, 7, and 8 are processed in both window 2 and window 3.

Setting the duration of a window to 16 seconds and the sliding interval to 12 seconds would produce this window arrangement:

Sliding time window with altered time interval

Here, the sliding interval determines that a new window is always created 12 seconds into the current window.

Tumbling windows

Tumbling windows are windows that don't overlap, as in this figure:

Tumbling time window

Tumbling windows don‘t overlap because a new window doesn’t begin until the current window has elapsed. For tumbling windows, you only need to specify the length or duration of the window but no sliding interval.

With tumbling windows, data are never processed in more than one window because the windows never overlap. Also, in the figure above, the duration of the window is 20 seconds.

Count windows

Count windows are specified on the basis of the number of operations rather than a time interval. A count window of 100 would mean that a window would elapse after 100 tuples have been processed, with no relation to clock time.

With count windows, this scenario (for a count window of 50) would be completely normal:

WindowTuples processedClock time
15010 seconds
25012 seconds
3501 hour, 12 minutes
4505 seconds

Time windows

Time windows differ from count windows because you need to specify a time duration (in seconds) rather than a number of tuples processed.

With time windows, this scenario (for a time window of 30 seconds) would be completely normal:

WindowTuples processedClock time
115030 seconds
25030 seconds
3030 seconds
437530 seconds

All window types

As explained above, windows differ along two axes: sliding (overlapping) vs. tumbling (non overlapping) and count vs. time. This produces four total types:

  1. Sliding time windows
  2. Sliding count windows
  3. Tumbling time windows
  4. Tumbling count windows

Resource allocation with the Heron Streamlet API

When creating topologies using the Streamlet API, there are three types of resources that you can specify:

  1. The number of containers into which the topology's physical plan will be split
  2. The total number of CPUs allocated to be used by the topology
  3. The total amount of RAM allocated to be used by the topology

For each topology, there are defaults for each resource type:

Number of containers11
RAM512 MB192MB

Allocating resources to topologies

For instructions on allocating resources to topologies, see the language-specific documentation for:

Data Model

Heron's original topology API required using a fundamentally tuple-driven data model. You can find more information in Heron's Data Model.