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:
Line Counter
,Single-Label Classification Model
,Slack Notification
,YOLO-World Model
,Image Convert Grayscale
,Absolute Static Crop
,Perspective Correction
,OpenAI
,Distance Measurement
,Image Slicer
,OpenAI
,Cosine Similarity
,Halo Visualization
,Byte Tracker
,Corner Visualization
,Email Notification
,Detections Classes Replacement
,Object Detection Model
,Template Matching
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Cache Set
,VLM as Classifier
,Depth Estimation
,Dynamic Zone
,Model Monitoring Inference Aggregator
,Cache Get
,Llama 3.2 Vision
,Anthropic Claude
,Crop Visualization
,Blur Visualization
,Dominant Color
,Image Threshold
,Stability AI Inpainting
,Relative Static Crop
,Path Deviation
,Clip Comparison
,Moondream2
,Twilio SMS Notification
,OCR Model
,Data Aggregator
,Google Vision OCR
,Property Definition
,Pixelate Visualization
,Stitch OCR Detections
,Image Slicer
,Time in Zone
,JSON Parser
,Webhook Sink
,Line Counter Visualization
,Byte Tracker
,Image Blur
,Florence-2 Model
,Delta Filter
,Detections Stabilizer
,LMM For Classification
,Instance Segmentation Model
,Keypoint Detection Model
,Bounding Rectangle
,Detections Stitch
,Stability AI Image Generation
,VLM as Classifier
,Identify Changes
,Detection Offset
,Camera Focus
,First Non Empty Or Default
,Polygon Zone Visualization
,Detections Filter
,Time in Zone
,Local File Sink
,Grid Visualization
,Instance Segmentation Model
,Trace Visualization
,Roboflow Custom Metadata
,Continue If
,Circle Visualization
,Environment Secrets Store
,Dimension Collapse
,Clip Comparison
,Triangle Visualization
,QR Code Detection
,Gaze Detection
,Line Counter
,Size Measurement
,Byte Tracker
,LMM
,Detections Consensus
,Velocity
,Buffer
,Stitch Images
,Segment Anything 2 Model
,Object Detection Model
,Model Comparison Visualization
,Keypoint Detection Model
,SIFT Comparison
,CLIP Embedding Model
,Expression
,CogVLM
,SmolVLM2
,VLM as Detector
,Image Preprocessing
,Pixel Color Count
,Background Color Visualization
,Barcode Detection
,Keypoint Visualization
,Color Visualization
,Multi-Label Classification Model
,Classification Label Visualization
,Camera Calibration
,Label Visualization
,Reference Path Visualization
,Single-Label Classification Model
,Google Gemini
,Roboflow Dataset Upload
,Identify Outliers
,Qwen2.5-VL
,Multi-Label Classification Model
,Detections Transformation
,SIFT Comparison
,Image Contours
,Polygon Visualization
,CSV Formatter
,SIFT
,Florence-2 Model
,Detections Merge
,Ellipse Visualization
,Mask Visualization
,Rate Limiter
,Bounding Box Visualization
,Dot Visualization
,VLM as Detector
,Path Deviation
- outputs:
Line Counter
,Single-Label Classification Model
,Slack Notification
,YOLO-World Model
,Image Convert Grayscale
,Absolute Static Crop
,Perspective Correction
,OpenAI
,Distance Measurement
,Image Slicer
,OpenAI
,Cosine Similarity
,Halo Visualization
,Byte Tracker
,Corner Visualization
,Email Notification
,Object Detection Model
,Detections Classes Replacement
,Template Matching
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,VLM as Classifier
,Cache Set
,Depth Estimation
,VLM as Detector
,Dynamic Zone
,Model Monitoring Inference Aggregator
,Cache Get
,Llama 3.2 Vision
,Anthropic Claude
,Crop Visualization
,Blur Visualization
,Dominant Color
,Image Threshold
,Stability AI Inpainting
,Relative Static Crop
,Path Deviation
,Clip Comparison
,Moondream2
,Twilio SMS Notification
,OCR Model
,Data Aggregator
,Google Vision OCR
,Pixelate Visualization
,Property Definition
,Stitch OCR Detections
,Image Slicer
,Time in Zone
,Webhook Sink
,JSON Parser
,Line Counter Visualization
,Byte Tracker
,Image Blur
,Florence-2 Model
,Delta Filter
,Detections Stabilizer
,LMM For Classification
,Instance Segmentation Model
,Keypoint Detection Model
,Detections Stitch
,Bounding Rectangle
,Stability AI Image Generation
,VLM as Classifier
,Identify Changes
,Detection Offset
,Camera Focus
,First Non Empty Or Default
,Polygon Zone Visualization
,Detections Filter
,Time in Zone
,Local File Sink
,Grid Visualization
,Instance Segmentation Model
,Trace Visualization
,Roboflow Custom Metadata
,Continue If
,Circle Visualization
,Dimension Collapse
,Clip Comparison
,Triangle Visualization
,QR Code Detection
,Gaze Detection
,Line Counter
,Size Measurement
,Byte Tracker
,LMM
,Detections Consensus
,Velocity
,Buffer
,Stitch Images
,Segment Anything 2 Model
,Object Detection Model
,Model Comparison Visualization
,Keypoint Detection Model
,SIFT Comparison
,CLIP Embedding Model
,Expression
,CogVLM
,SmolVLM2
,VLM as Detector
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Barcode Detection
,Pixel Color Count
,Color Visualization
,Multi-Label Classification Model
,Classification Label Visualization
,Camera Calibration
,Label Visualization
,Reference Path Visualization
,Single-Label Classification Model
,Google Gemini
,Roboflow Dataset Upload
,Qwen2.5-VL
,Multi-Label Classification Model
,Detections Transformation
,SIFT Comparison
,Image Contours
,Polygon Visualization
,CSV Formatter
,SIFT
,Florence-2 Model
,Detections Merge
,Ellipse Visualization
,Mask Visualization
,Rate Limiter
,Bounding Box Visualization
,Dot Visualization
,Identify Outliers
,Path Deviation
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
}