th2-data-services

Table of Contents

• Table of Contents
• 1. Introduction
• 2. Getting started
  • 2.1. Installation
    • Core
    • Data sources (providers)
    • GRPC provider warning
      • Reasons for the restriction
  • 2.2. Example
  • 2.3. Short theory
    • Terms
    • Concept
    • Stream operations
      • Pipelining
      • Internal iteration
    • Data caching
      • Forced caching
    • EventsTree and collections
      • EventsTree
      • Collections
      • Hints
  • 2.4. Links
• 3. API
• 4. Examples
  • 4.1. Notebooks
  • 4.2. *.py

1. Introduction

This repository is a library for creating th2-data-services applications.

The library used to analyze stream data using aggregate operations mainly from the "Report Data Provider". Data Services allows you to manipulate the stream data processing workflow using pipelining.

The library allows you:

• Natively connect to "Report Data Provider" via ProviderDataSource class and extract TH2 Events/Messages via commands
• Work with iterable objects (list, tuple, etc including files) via Data object using its features
• Manipulate the workflow to make some analysis by Data object methods
• Build Event Trees (EventsTreeCollection class)

Workflow manipulation tools allows you:

• Filtering stream data (Data.filter method)
• Transforming stream data (Data.map method)
• Limiting the number of processed streaming data (Data.limit method)

There is also another part of data services

• th2-data-services-utils. It's a set of tools to perform the most common analysis tasks.

2. Getting started

2.1. Installation

Core

• From PyPI (pip)
  This package can be found on PyPI.

  pip install th2-data-services
  

• From Source

  git clone https://github.com/th2-net/th2-data-services
  pip install th2-data-services/
  

Data sources (providers)

Since v1.3.0, the library doesn't provide data source dependencies.

You should provide it manually during installation. You just need to add square brackets after library name and put dependency name.

pip install th2-data-services[dependency_name]

Dependencies list

dependency name	provider version
RDP5	5
RDP6	6

Example

pip install th2-data-services[rdp5]

GRPC provider warning

This library has ability to interact with several versions of grpc providers, but it's limited by installed version of th2_grpc_data_provider package version. You can use only appropriate version of provider api, which is compatible with installed version of th2_grpc_data_provider.

By default, th2_data_services uses the latest available version of provider api version.

Reasons for the restriction

1. Two different versions of th2_grpc_data_provider can't be installed in the same virtual environment;
2. Two different versions of package th2_grpc_data_provider may depend on different versions of packages th2_grpc_common;
3. In the case of using another package in the process of using th2_data_services (for example th2_common), which also depends on th2_grpc_common, a version conflict may occur (both at the Python level and at the Protobuf level).

2.2. Example

A good, short example is worth a thousand words.

This example works with Events, but you also can do the same actions with Messages.

The following example as a file.

from collections import Generator
from typing import Tuple, List, Optional
from datetime import datetime

from th2_data_services import Data
from th2_data_services.events_tree import EventsTree
from th2_data_services.provider.v5.data_source.http import HTTPProvider5DataSource
from th2_data_services.provider.v5.commands import http as commands
from th2_data_services.provider.v5.events_tree import EventsTreeCollectionProvider5, ParentEventsTreeCollectionProvider5
from th2_data_services.provider.v5.filters.event_filters import NameFilter, TypeFilter, FailedStatusFilter
from th2_data_services.provider.v5.filters.message_filters import BodyFilter

# [0] Lib configuration
# [0.1] Interactive or Script mode
# If you use the lib in interactive mode (jupyter, ipython) it's recommended to set the special
# global parameter to True. It'll keep cache files if something went wrong.
import th2_data_services

th2_data_services.INTERACTIVE_MODE = True

# [1] Create DataSource object to connect to rpt-data-provider.
DEMO_HOST = "10.100.66.66"  # th2-kube-demo  Host port where rpt-data-provider is located.
DEMO_PORT = "30999"  # Node port of rpt-data-provider.
data_source = HTTPProvider5DataSource(f"http://{DEMO_HOST}:{DEMO_PORT}")

START_TIME = datetime(
    year=2021, month=6, day=17, hour=9, minute=44, second=41, microsecond=692724
)  # Datetime in utc format.
END_TIME = datetime(year=2021, month=6, day=17, hour=12, minute=45, second=50)

# [2] Get events or messages from START_TIME to END_TIME.
# [2.1] Get events.
events: Data = data_source.command(
    commands.GetEvents(
        start_timestamp=START_TIME,
        end_timestamp=END_TIME,
        attached_messages=True,
        # Use Filter class to apply rpt-data-provider filters.
        # Do not use multiple classes of the same type.
        filters=[
            TypeFilter("Send message"),
            NameFilter(["ExecutionReport", "NewOrderSingle"]),  # You can use multiple values.
            FailedStatusFilter(),
        ],
    )
)

# [2.2] Get messages.
messages: Data = data_source.command(
    commands.GetMessages(
        start_timestamp=START_TIME,
        end_timestamp=END_TIME,
        attached_events=True,
        stream=["demo-conn2"],
        filters=BodyFilter("195"),  # Filter message if there is a substring '195' in the body.
    )
)

# [3] Work with a Data object.
# [3.1] Filter.
filtered_events: Data = events.filter(lambda e: e["body"] != [])  # Filter events with empty body.


# [3.2] Map.
def transform_function(record):
    return {"eventName": record["eventName"], "successful": record["successful"]}


filtered_and_mapped_events = filtered_events.map(transform_function)

# [3.3] Data pipeline.
#       Instead of doing data transformations step by step you can do it in one line.
filtered_and_mapped_events_by_pipeline = events.filter(lambda e: e["body"] != []).map(transform_function)

# Content of these two Data objects should be equal.
assert list(filtered_and_mapped_events) == list(filtered_and_mapped_events_by_pipeline)

# [3.4] Sift. Skip the first few items or limit them.
events_from_11_to_end: Generator = events.sift(skip=10)
only_first_10_events: Generator = events.sift(limit=10)

# [3.5] Changing cache status.
events.use_cache(True)
# or just
events.use_cache()  # If you want to activate cache.

# [3.6] Walk through data.
for event in events:
    # Do something with event (event is a dict).
    print(event)
# After first iteration the events has a cache file.
# Now they will be used in the cache in the next iteration.

# [3.7] Get number of the elements in the Data object.
number_of_events = events.len

# [3.8] Check that Data object isn't empty.
# The data source should be not empty.
assert events.is_empty is False

# [3.9] Convert Data object to the list of elements(events or messages).
# Be careful, this can take too much memory.
events_list = list(events)

# [3.10] Get event/message by id.
desired_event = "9ce8a2ff-d600-4366-9aba-2082cfc69901:ef1d722e-cf5e-11eb-bcd0-ced60009573f"
desired_events = [
    "deea079b-4235-4421-abf6-6a3ac1d04c76:ef1d3a20-cf5e-11eb-bcd0-ced60009573f",
    "a34e3cb4-c635-4a90-8f42-37dd984209cb:ef1c5cea-cf5e-11eb-bcd0-ced60009573f",
]
desired_message = "demo-conn1:first:1619506157132265837"
desired_messages = [
    "demo-conn1:first:1619506157132265836",
    "demo-conn1:first:1619506157132265833",
]

data_source.command(commands.GetEventById(desired_event))  # Returns 1 event (dict).
data_source.command(commands.GetEventsById(desired_events))  # Returns 2 events list(dict).

data_source.command(commands.GetMessageById(desired_message))  # Returns 1 message (dict).
data_source.command(commands.GetMessagesById(desired_messages))  # Returns 2 messages list(dict).

# [3.11] The cache inheritance.
# Creates a new Data object that will use cache from the events Data object.
events_filtered: Data = events.filter(lambda record: record.get("batchId"))

# New Data objects don't use their own cache by default but use the cache of the parent Data object.
# Use use_cache method to activate caching.
# After that, the Data object will create its own cache file.
events_filtered.use_cache()

list(events_filtered)  # Just to iterate Data object (cache file will be created).

filtered_events_types = events_filtered.map(lambda record: {"eventType": record.get("eventType")})

events_without_types_with_batch = filtered_events_types.filter(lambda record: not record.get("eventType"))
events_without_types_with_batch.use_cache()

# [3.12] Data objects joining.
# You have the following 3 Data objects.
d1 = Data([1, 2, 3])
d2 = Data(["a", {"id": 123}, "c"])
d3 = Data([7, 8, 9])
# You can join Data objects in following ways.
data_via_init = Data([d1, d2, d3])
data_via_add = d1 + d2 + d3
data_with_non_data_obj_via_init = Data([d1, ["a", {"id": 123}, "c"], d3])
data_with_non_data_obj_via_add = d1 + ["a", {"id": 123}, "c"] + d3

# [3.13] Build and read Data object cache files.
events.build_cache("cache_filename_or_path")
data_obj_from_cache = Data.from_cache_file("cache_filename_or_path")


# [4] Working with EventsTree and EventsTreeCollection.
# [4.1] Building the EventsTreeCollection.

# If you don't specify data_source for the tree then it won't recover detached events.
collection = EventsTreeCollectionProvider5(events)

# Detached events isn't empty.
assert collection.detached_events

collection = EventsTreeCollectionProvider5(events, data_source=data_source)
# Detached events are empty because they were recovered.
assert not collection.detached_events

# The collection has EventsTrees each with a tree of events.
# Using Collection and EventsTrees, you can work flexibly with events.

# [4.1.1] Get leaves of all trees.
leaves: Tuple[dict] = collection.get_leaves()

# [4.1.2] Get roots ids of all trees.
roots: List[str] = collection.get_roots_ids()

# [4.1.3] Find an event in all trees.
find_event: Optional[dict] = collection.find(lambda event: "Send message" in event["eventType"])

# [4.1.4] Find all events in all trees. There is also iterable version 'findall_iter'.
find_events: List[dict] = collection.findall(lambda event: event["successful"] is True)

# [4.1.5] Find an ancestor of the event.
ancestor: Optional[dict] = collection.find_ancestor(
    "8bbe3717-cf59-11eb-a3f7-094f904c3a62", filter=lambda event: "RootEvent" in event["eventName"]
)

# [4.1.6] Get children of the event. There is also iterable version 'get_children_iter'.
children: Tuple[dict] = collection.get_children("814422e1-9c68-11eb-8598-691ebd7f413d")

# [4.1.7] Get subtree for specified event.
subtree: EventsTree = collection.get_subtree("8e23774d-cf59-11eb-a6e3-55bfdb2b3f21")

# [4.1.8] Get full path to the event.
# Looks like [ancestor_root, ancestor_level1, ancestor_level2, event]
event_path: List[dict] = collection.get_full_path("8e2524fa-cf59-11eb-a3f7-094f904c3a62")

# [4.1.9] Get parent of the event.
parent = collection.get_parent("8e2524fa-cf59-11eb-a3f7-094f904c3a62")

# [4.1.10] Append new event to the collection.
collection.append_event(
    event={
        "eventId": "a20f5ef4-c3fe-bb10-a29c-dd3d784909eb",
        "parentEventId": "8e2524fa-cf59-11eb-a3f7-094f904c3a62",
        "eventName": "StubEvent",
    }
)

# [4.1.11] Show the entire collection.
collection.show()

# [4.2] Working with the EventsTree.
# EventsTree has the same methods as EventsTreeCollection, but only for its own tree.

# [4.2.1] Get collection trees.
trees: List[EventsTree] = collection.get_trees()
tree: EventsTree = trees[0]

# But EventsTree provides a work with the tree, but does not modify it.
# If you want to modify the tree, use EventsTreeCollections.

# [4.3] Working with ParentlessTree.
# ParentlessTree is EventsTree which has detached events with stubs.
parentless_trees: List[EventsTree] = collection.get_parentless_trees()

# [4.4] Working with ParentEventsTreeCollection.
# ParentEventsTreeCollection is a tree like EventsTreeCollection but it has only events that have references.
collection = ParentEventsTreeCollectionProvider5(events, data_source=data_source)

collection.show()

2.3. Short theory

The library provides tools for handling stream data. What’s a stream? It's a sequence of elements from a source that supports aggregate operations.

Terms

• Data object: An instance of Data class which is wrapper under stream.
• Sequence of elements: A Data object provides an interface to a sequenced set of values of a specific element type. Stream inside the Data object don’t actually store elements; they are computed on demand.
• DataSource: Any source of data. E.g. Report Data Provider, collections, arrays, or I/O resources.
• ProviderDataSource: The DataSource object whose source is Report Data Provider.
• SourceAPI: Each source has its own API to retrieve data. SourceAPI is a class that provide API for some data source.
• Commands: Objects that provide user-friendly interfaces for getting some data from DataSource. Commands use SourceAPI to achieve it.
• Adapters: It's similar to function for Data.map method. Adoptable commands used it to update the data stream.
• Aggregate operations: Common operations such as filter, map, limit and so on.
• Workflow: An ordered set of Aggregate operations.

Concept

The library describes the high-level interfaces ISourceAPI, IDataSource, ICommand, IAdapter.

Any data source must be described by the IDataSource abstract class. These can be FileDataSource, CSVDataSource, _ DBDataSource_ and other.

Usually, data sources have some kind of API. Databases - provide SQL language, when working with a file, you can read line by line, etc. This API is described by the ISourceAPI class. Because different versions of the same data source may have different API, it is better to create a class for each version.

Generally, data source APIs are hidden behind convenient interfaces. The role of these interfaces is played by ICommand classes.

IAdapter classes transform data stream like functions for Data.map method. Essentially it's the same thing but more flexible.

Thus, the native ProviderDataSource and the set of commands for it are described. This approach provides great opportunities for extension. You can easily create your own unique commands for ProviderDataSource, as well as entire DataSource classes.

Stream operations

Furthermore, stream operations have two fundamental characteristics that make them very different from collection operations: Pipelining and Internal iteration.

Pipelining

Many stream operations return a stream themselves. This allows operations to be chained to form a larger pipeline.

Internal iteration

In contrast to collections, which are iterated explicitly (external iteration), stream operations do the iteration behind the scenes for you. Note, it doesn't mean you cannot iterate the Data object.

Data caching

The Data object provides the ability to use the cache. The cache works for each Data object, that is, you choose which Data object you want to save. The Data object cache is saved after the first iteration, but the iteration source may be different.

If you don't use the cache, your source will be the data source you have in the Data Object. But if you use the cache, your source can be the data source, the parent cache, or own cache:

• The data source: If the Data Object doesn't have a parent cache and its cache.
• The parent cache: If the Data Object has a parent cache. It doesn't matter what position the parent cache has in inheritance. Data Object understands whose cache it is and executes the part of the workflow that was not executed.
• The own cache: If it is not the first iteration of this Data object.

Note that the cache state of the Data object is not inherited.

Forced caching

You can tell DS to cache data to specific cache file, which won't be deleted after script end:

import datetime

from th2_data_services import Data
from th2_data_services.provider.v5.commands import http
from th2_data_services.provider.v5.data_source import HTTPProvider5DataSource


data_source = HTTPProvider5DataSource("http://HOST:PORT")
events: Data = data_source.command(
    http.GetEvents(
        start_timestamp=datetime.datetime.utcnow() - datetime.timedelta(minutes=5),
        end_timestamp=datetime.datetime.utcnow(),
        attached_messages=True,
        cache=True,
    )
)
events.build_cache("my_cache.pickle")

Later you can create Data object from this cache file and use it as usual:

from th2_data_services import Data

events = Data.from_cache_file("my_cache.pickle")

for event_id in events.filter(lambda x: x["eventType"] == "Verification").map(lambda x: x["eventId"]):
    print(event_id)

EventsTree and collections

EventsTree

EventsTree is a tree-based data structure of events. It allows you get children and parents of event, display tree, get full path to event etc.

Details:

• EventsTree contains all events in memory.
• To reduce memory usage an EventsTreeCollection delete the 'body' field from events, but you can preserve it specify ' preserve_body'.
• Tree has some important terms:
  1. Ancestor is any relative of the event up the tree (grandparent, parent etc.).
  2. Parent is only the first relative of the event up the tree.
  3. Child is the first relative of the event down the tree.

Take a look at the following HTML tree to understand them.

 <body> <!-- ancestor (grandparent), but not parent -->
     <div> <!-- parent & ancestor -->
         <p>Hello, world!</p> <!-- child -->
         <p>Goodbye!</p> <!-- sibling -->
     </div>
 </body>

Collections

EventsTreeCollection is a collection of EventsTrees. The collection builds a few EventsTree by passed Data object. Although you can change the tree directly, it's better to do it through collections because they are aware of detached_events and can solve some events dependencies. The collection has similar features like a single EventsTree but applying them for all EventsTrees.

ParentEventsTreeCollection is a collection similar to EventsTreeCollection but containing only parent events that are referenced in the data stream. It will be working data in the collection and trees of collection. The collection has features similar to EventsTreeCollection.

Details:

• The collection has a feature to recover events. All events that are not in the received data stream, but which are referenced will be loaded from the data source.
• If you haven't passed a DataSource object then the recovery of events will not occur.
• You can take detached_events to see which events are missing. It looks like {parent_id: [events are referenced]}
• If you want, you can build parentless trees where the missing events are stubbed instead. Just use get_parentless_trees().

Requirements:

1. Events have to have event_name, event_id, parent_event_id fields, which are described in the passed event_struct object.

Hints

• Remove all unnecessary fields from events before passing to a collection to reduce memory usage.
• Use show() method to print the tree in tree-like view.
• Note that the get_x methods will raise an exception if you pass an unknown event id, unlike the find_x methods ( they return None).
• If you want to know that specified event exists, use the python in keyword (e.g. 'event-id' in events_tree).
• Use the python len keyword to get events number in the tree.

2.4. Links

• Report Data Provider
• Th2 Data Services Utils

3. API

If you are looking for classes description see the API Documentation.

4. Examples

4.1. Notebooks

• notebook_0.ipynb

4.2. *.py

• get_started_example.py