Data Aggregator¶
Class: DataAggregatorBlockV1
Source: inference.core.workflows.core_steps.analytics.data_aggregator.v1.DataAggregatorBlockV1
The Data Aggregator block collects and processes data from Workflows to generate time-based statistical summaries. It allows users to define custom aggregation strategies over specified intervals, making it suitable for creating analytics on data streams.
The block enables:
-
feeding it with data from other Workflow blocks and applying in-place operations (for instance to extract desired values out of model predictions)
-
using multiple aggregation modes, including
sum
,avg
,max
,min
,count
and others -
specifying aggregation interval flexibly
Feeding Data Aggregator¶
You can specify the data to aggregate by referencing input sources using the data
field. Optionally,
for each specified data
input you can apply chain of UQL operations with data_operations
property.
For example, the following configuration:
data = {
"predictions_model_a": "$steps.model_a.predictions",
"predictions_model_b": "$steps.model_b.predictions",
}
data_operations = {
"predictions_model_a": [
{"type": "DetectionsPropertyExtract", "property_name": "class_name"}
],
"predictions_model_b": [{"type": "SequenceLength"}]
}
on each step run will at first take predictions_model_a
to extract list of detected classes
and calculate the number of predicted bounding boxes for predictions_model_b
.
Specifying data aggregations¶
For each input data referenced by data
property you can specify list of aggregation operations, that
include:
-
sum
: Taking the sum of values (requires data to be numeric) -
avg
: Taking the average of values (requires data to be numeric) -
max
: Taking the max of values (requires data to be numeric) -
min
: Taking the min of values (requires data to be numeric) -
count
: Counting the values - if provided value is list - operation will add length of the list into aggregated state -
distinct
: deduplication of encountered values - providing list of unique values in the output. If aggregation data is list - operation will add each element of the list into aggregated state. -
count_distinct
: counting occurrences of distinct values - providing number of different values that were encountered. If aggregation data is list - operation will add each element of the list into aggregated state. -
count_distinct
: counting distinct values - providing number of different values that were encountered. If aggregation data is list - operation will add each element of the list into aggregated state. -
values_counts
: counting occurrences of each distinct value - providing dictionary mapping each unique value encountered into the number of observations. If aggregation data is list - operation will add each element of the list into aggregated state. -
values_difference
: calculates the difference between max and min observed value (requires data to be numeric)
If we take the data
and data_operations
from the example above and specify aggregation_mode
in the following way:
aggregation_mode = {
"predictions_model_a": ["distinct", "count_distinct"],
"predictions_model_b": ["avg"],
}
Our aggregation report will contain the following values:
{
"predictions_model_a_distinct": ["car", "person", "dog"],
"predictions_model_a_count_distinct": {"car": 378, "person": 128, "dog": 37},
"predictions_model_b_avg": 7.35,
}
where:
-
predictions_model_a_distinct
provides distinct classes predicted by model A in aggregation window -
predictions_model_a_count_distinct
provides number of classes instances predicted by model A in aggregation window -
predictions_model_b_avg
provides average number of bounding boxes predicted by model B in aggregation window
Interval nature of the block¶
Block behaviour is dictated by internal 'clock'
Behaviour of this block differs from other, more classical blocks which output the data for each input. Data Aggregator block maintains its internal state that dictates when the data will be produced, flushing internal aggregation state of the block.
You can expect that most of the times, once fed with data, the block will produce empty outputs, effectively terminating downstream processing:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ Data Aggregator │ ----> <Empty>
... │ │ ----> <Empty>
--- input_batch[n] ----> └───────────────────────┘ ----> <Empty>
But once for a while, the block will yield aggregated data and flush its internal state:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ Data Aggregator │ ----> {<aggregated_report>}
... │ │ ----> <Empty> # first datapoint added to new state
--- input_batch[n] ----> └───────────────────────┘ ----> <Empty>
Setting the aggregation interval is possible with interval
and interval_unit
property.
interval
specifies the length of aggregation window and interval_unit
bounds the interval
value
into units. You can specify the interval based on:
-
time elapse: using
["seconds", "minutes", "hours"]
asinterval_unit
will make the Data Aggregator to yield the aggregated report based on time that elapsed since last report was released - this setting is relevant for processing of video streams. -
number of runs: using
runs
asinterval_unit
- this setting is relevant for processing of video files, as in this context wall-clock time elapse is not the proper way of getting meaningful reports.
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/data_aggregator@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
data_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. element of the data. | ❌ |
aggregation_mode |
Dict[str, List[str]] |
Lists of aggregation operations to apply on each input data. | ❌ |
interval_unit |
str |
Unit to measure interval . |
❌ |
interval |
int |
Length of aggregation interval. | ❌ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Data Aggregator
in version v1
.
- inputs:
Model Monitoring Inference Aggregator
,Label Visualization
,Florence-2 Model
,Triangle Visualization
,Image Blur
,OCR Model
,Detections Transformation
,Line Counter Visualization
,Circle Visualization
,Relative Static Crop
,Detections Stitch
,Trace Visualization
,Byte Tracker
,Object Detection Model
,Clip Comparison
,Detection Offset
,Detections Consensus
,Velocity
,Dominant Color
,Stitch Images
,Bounding Rectangle
,Detections Filter
,Cosine Similarity
,Segment Anything 2 Model
,Roboflow Custom Metadata
,Single-Label Classification Model
,VLM as Classifier
,Local File Sink
,Crop Visualization
,Color Visualization
,Image Slicer
,Google Gemini
,Dynamic Crop
,Multi-Label Classification Model
,Continue If
,Instance Segmentation Model
,Keypoint Detection Model
,SmolVLM2
,Google Vision OCR
,Corner Visualization
,Overlap Filter
,Polygon Zone Visualization
,Byte Tracker
,Camera Focus
,Grid Visualization
,Perspective Correction
,Stability AI Image Generation
,Cache Get
,Image Slicer
,CSV Formatter
,OpenAI
,Blur Visualization
,Time in Zone
,Slack Notification
,SIFT Comparison
,Pixel Color Count
,Detections Stabilizer
,Anthropic Claude
,QR Code Detection
,Buffer
,Bounding Box Visualization
,Distance Measurement
,Twilio SMS Notification
,Email Notification
,Object Detection Model
,LMM For Classification
,SIFT Comparison
,VLM as Detector
,LMM
,Florence-2 Model
,Depth Estimation
,CogVLM
,Model Comparison Visualization
,Cache Set
,First Non Empty Or Default
,Data Aggregator
,Barcode Detection
,Environment Secrets Store
,Multi-Label Classification Model
,Path Deviation
,Rate Limiter
,Dimension Collapse
,Gaze Detection
,Reference Path Visualization
,Llama 3.2 Vision
,Expression
,Detections Merge
,Polygon Visualization
,Roboflow Dataset Upload
,Time in Zone
,Identify Outliers
,SIFT
,Image Threshold
,Path Deviation
,Property Definition
,CLIP Embedding Model
,Keypoint Visualization
,Ellipse Visualization
,JSON Parser
,OpenAI
,Instance Segmentation Model
,Dot Visualization
,Roboflow Dataset Upload
,Keypoint Detection Model
,Stability AI Inpainting
,Line Counter
,Identify Changes
,Single-Label Classification Model
,Template Matching
,Background Color Visualization
,Delta Filter
,VLM as Detector
,Line Counter
,Qwen2.5-VL
,Clip Comparison
,Dynamic Zone
,Classification Label Visualization
,Image Convert Grayscale
,Image Preprocessing
,Byte Tracker
,OpenAI
,YOLO-World Model
,Stability AI Outpainting
,Perception Encoder Embedding Model
,Size Measurement
,Webhook Sink
,Moondream2
,Camera Calibration
,Mask Visualization
,Pixelate Visualization
,Stitch OCR Detections
,PTZ Tracking (ONVIF)
,Image Contours
,Detections Classes Replacement
,Absolute Static Crop
,Halo Visualization
,VLM as Classifier
- outputs:
Model Monitoring Inference Aggregator
,Label Visualization
,Florence-2 Model
,Triangle Visualization
,Image Blur
,OCR Model
,Detections Transformation
,Line Counter Visualization
,Circle Visualization
,Relative Static Crop
,Detections Stitch
,Trace Visualization
,Byte Tracker
,Object Detection Model
,Clip Comparison
,Detections Consensus
,Detection Offset
,Velocity
,Stitch Images
,Dominant Color
,Bounding Rectangle
,Detections Filter
,Cosine Similarity
,Segment Anything 2 Model
,Roboflow Custom Metadata
,Single-Label Classification Model
,Local File Sink
,VLM as Classifier
,Crop Visualization
,Color Visualization
,Image Slicer
,Google Gemini
,Dynamic Crop
,Multi-Label Classification Model
,Instance Segmentation Model
,Continue If
,Keypoint Detection Model
,SmolVLM2
,Google Vision OCR
,Corner Visualization
,Overlap Filter
,Polygon Zone Visualization
,Byte Tracker
,Camera Focus
,Stability AI Image Generation
,Perspective Correction
,Grid Visualization
,Cache Get
,Image Slicer
,CSV Formatter
,OpenAI
,Blur Visualization
,Time in Zone
,Slack Notification
,SIFT Comparison
,Pixel Color Count
,Detections Stabilizer
,Anthropic Claude
,QR Code Detection
,Buffer
,Bounding Box Visualization
,Distance Measurement
,Twilio SMS Notification
,Email Notification
,Object Detection Model
,LMM For Classification
,SIFT Comparison
,VLM as Detector
,LMM
,Florence-2 Model
,Depth Estimation
,CogVLM
,Model Comparison Visualization
,Cache Set
,First Non Empty Or Default
,Data Aggregator
,Barcode Detection
,Multi-Label Classification Model
,Path Deviation
,Dimension Collapse
,Rate Limiter
,Gaze Detection
,Reference Path Visualization
,Llama 3.2 Vision
,Polygon Visualization
,Roboflow Dataset Upload
,Time in Zone
,Identify Outliers
,Expression
,Detections Merge
,SIFT
,Image Threshold
,Path Deviation
,Property Definition
,CLIP Embedding Model
,Keypoint Visualization
,Ellipse Visualization
,JSON Parser
,OpenAI
,Instance Segmentation Model
,Dot Visualization
,Roboflow Dataset Upload
,Keypoint Detection Model
,Identify Changes
,Stability AI Inpainting
,Line Counter
,Single-Label Classification Model
,Template Matching
,Background Color Visualization
,Delta Filter
,VLM as Detector
,Line Counter
,Qwen2.5-VL
,Clip Comparison
,Dynamic Zone
,Classification Label Visualization
,Image Convert Grayscale
,Image Preprocessing
,Byte Tracker
,OpenAI
,YOLO-World Model
,Stability AI Outpainting
,Perception Encoder Embedding Model
,Size Measurement
,Webhook Sink
,Moondream2
,Camera Calibration
,Mask Visualization
,Pixelate Visualization
,Stitch OCR Detections
,PTZ Tracking (ONVIF)
,Image Contours
,Detections Classes Replacement
,Absolute Static Crop
,Halo Visualization
,VLM as Classifier
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Data Aggregator
in version v1
has.
Bindings
Example JSON definition of step Data Aggregator
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/data_aggregator@v1",
"data": {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names"
},
"data_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"aggregation_mode": {
"predictions": [
"distinct",
"count_distinct"
]
},
"interval_unit": "seconds",
"interval": 10
}