storey 1.3.0

Async flows

Storey

Storey is an asynchronous streaming library, for real time event processing and feature extraction.

In This Document

• API Walkthrough
• Usage Examples

▶ For more information, see the Storey Python package documentation.

API Walkthrough

A Storey flow consist of steps linked together by the build_flow function, each doing it's designated work.

Supported Steps

Input Steps

• SyncEmitSource
• AsyncEmitSource
• CSVSource
• ParquetSource
• DataframeSource

Processing Steps

• Filter
• Map
• FlatMap
• MapWithState
• Batch(max_events, timeout) - Batches events. This step emits a batch every max_events events, or when timeout seconds have passed since the first event in the batch was received.
• Choice
• JoinWithV3IOTable
• SendToHttp
• AggregateByKey(aggregations,cache, key=None, emit_policy=EmitEveryEvent(), augmentation_fn=None) - This step aggregates the data into the cache object provided for later persistence, and outputs an event enriched with the requested aggregation features.
• QueryByKey(features, cache, key=None, augmentation_fn=None, aliases=None) - Similar to to AggregateByKey, but this step is for serving only and does not aggregate the event.
• NoSqlTarget(table) - Persists the data in table to its associated storage by key.
• Extend
• JoinWithTable

Output Steps

• Complete
• Reduce
• StreamTarget
• CSVTarget
• ReduceToDataFrame
• TSDBTarget
• ParquetTarget

Usage Examples

Using Aggregates

The following example reads user data, creates features using Storey's aggregates, persists the data to V3IO and emits events containing the features to a V3IO Stream for further processing.

from storey import build_flow, SyncEmitSource, Table, V3ioDriver, AggregateByKey, FieldAggregator, NoSqlTarget
from storey.dtypes import SlidingWindows

v3io_web_api = "https://webapi.change-me.com"
v3io_acceess_key = "1284ne83-i262-46m6-9a23-810n41f169ea"
table_object = Table("/projects/my_features", V3ioDriver(v3io_web_api, v3io_acceess_key))

def enrich(event, state):
    if "first_activity" not in state:
        state["first_activity"] = event.time
    event.body["time_since_activity"] = (event.body["time"] - state["first_activity"]).seconds
    state["last_event"] = event.time
    event.body["total_activities"] = state["total_activities"] = state.get("total_activities", 0) + 1
    return event, state

controller = build_flow([
    SyncEmitSource(),
    MapWithState(table_object, enrich, group_by_key=True, full_event=True),
    AggregateByKey([FieldAggregator("number_of_clicks", "click", ["count"],
                                    SlidingWindows(["1h","2h", "24h"], "10m")),
                    FieldAggregator("purchases", "purchase_amount", ["avg", "min", "max"],
                                    SlidingWindows(["1h","2h", "24h"], "10m")),
                    FieldAggregator("failed_activities", "activity", ["count"],
                                    SlidingWindows(["1h"], "10m"),
                                    aggr_filter=lambda element: element["activity_status"] == "fail"))],
                   table_object,
                   time_field="time"),
    NoSqlTarget(table_object),
    StreamTarget(V3ioDriver(v3io_web_api, v3io_acceess_key), "features_stream")
]).run()

We can also create a serving function, which sole purpose is to read data from the feature store and emit it further

controller = build_flow([
    SyncEmitSource(),
    QueryAggregationByKey([FieldAggregator("number_of_clicks", "click", ["count"],
                                           SlidingWindows(["1h","2h", "24h"], "10m")),
                           FieldAggregator("purchases", "purchase_amount", ["avg", "min", "max"],
                                           SlidingWindows(["1h","2h", "24h"], "10m")),
                           FieldAggregator("failed_activities", "activity", ["count"],
                                           SlidingWindows(["1h"], "10m"),
                                           aggr_filter=lambda element: element["activity_status"] == "fail"))],
                           table_object,
                           time_field="time")
]).run()