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