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diff --git a/python-storey.spec b/python-storey.spec new file mode 100644 index 0000000..4f6a36d --- /dev/null +++ b/python-storey.spec @@ -0,0 +1,401 @@ +%global _empty_manifest_terminate_build 0 +Name: python-storey +Version: 1.3.15 +Release: 1 +Summary: Async flows +License: Apache +URL: https://github.com/mlrun/storey +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/bc/2d/de49b705e44b582a7599beff4d51e2aa98984d27cff3ac38ea5315b4b265/storey-1.3.15.tar.gz +BuildArch: noarch + +Requires: python3-aiohttp +Requires: python3-v3io +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-pyarrow +Requires: python3-v3io-frames +Requires: python3-v3iofs +Requires: python3-xxhash +Requires: python3-adlfs +Requires: python3-kafka-python +Requires: python3-lupa +Requires: python3-redis +Requires: python3-s3fs +Requires: python3-sqlalchemy + +%description +# Storey + +[](https://github.com/mlrun/storey/actions?query=workflow%3ACI) + +Storey is an asynchronous streaming library, for real time event processing and feature extraction. + +#### In This Document + +- [API Walkthrough](#api-walkthrough) +- [Usage Examples](#examples) + +▶ For more information, see the [Storey Python package documentation](https://storey.readthedocs.io). + +<a id="api-walkthrough"></a> +## 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` + + +<a id="examples"></a> +## 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. + +```python +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 + +```python +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() +``` + + +%package -n python3-storey +Summary: Async flows +Provides: python-storey +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-storey +# Storey + +[](https://github.com/mlrun/storey/actions?query=workflow%3ACI) + +Storey is an asynchronous streaming library, for real time event processing and feature extraction. + +#### In This Document + +- [API Walkthrough](#api-walkthrough) +- [Usage Examples](#examples) + +▶ For more information, see the [Storey Python package documentation](https://storey.readthedocs.io). + +<a id="api-walkthrough"></a> +## 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` + + +<a id="examples"></a> +## 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. + +```python +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 + +```python +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() +``` + + +%package help +Summary: Development documents and examples for storey +Provides: python3-storey-doc +%description help +# Storey + +[](https://github.com/mlrun/storey/actions?query=workflow%3ACI) + +Storey is an asynchronous streaming library, for real time event processing and feature extraction. + +#### In This Document + +- [API Walkthrough](#api-walkthrough) +- [Usage Examples](#examples) + +▶ For more information, see the [Storey Python package documentation](https://storey.readthedocs.io). + +<a id="api-walkthrough"></a> +## 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` + + +<a id="examples"></a> +## 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. + +```python +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 + +```python +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() +``` + + +%prep +%autosetup -n storey-1.3.15 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-storey -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.15-1 +- Package Spec generated |