– Open source stream processor framework developed by the Apache Software Foundation (2016) – Data streams with high data volume can be processed and analyzed with low delay and high speed

Core functions

– diverse, specialized APIs: → DataStream API (Stream Processing) → ProcessFunctions (control of states and time; event states can be saved and timers can be added for future calculations) → Table API → SQL API → provides a rich set of connectors to various storage systems such as Kafka, Kinesis, Kubernetes, YARN, HDFS, Elasticsearch, and JDBC database systems → REST API

Stream Processing

== Data is processed continuously with a short delay → without intermediate storage of the data in separate databases – several data streams can be processed in parallel – Each stream can be used to derive own follow-up actions and analyses

Architecture

Data can be processed as unbounded or bounded streams:

Unbounded stream

have a start but no defined end

must be continuously processed

Bounded stream

have a defined start and end

can be processed by ingesting all data before performing any computations(== batch processing)

– Flink automatically identifies the required resources based on the application’s configured parallelism and requests them from the resource manager.

–In case of a failure, Flink replaces the failed container by requesting new resources.

– Stateful Flink applications are optimized for local state access

PyTorch BigGraph – The graph is a data structure that can be used to clearly represent relationships between data objects as nodes and edges. These structures can contain billions of nodes and edges in an industrial context.

So how can the multidimensional data relationships be accessed in a meaningful way? Graph embedding offers one possibility for dimension reduction. This is a sequence of different algorithms with the goal of reducing the graph’s property relations to vector spaces. These embedding methods usually run unsupervised. If there is a large property similarity, two points should also be close to each other in the vector space.

The reduced feature information can then be further processed with additional machine learning algorithms.

What is PyTorch BigGraph?

Facebook offers PyTorch BigGraph, an open source library that can be used to create very performant graph embeddings for extremely large graphs.

It is a distributed system that can unsupervised learn graph embeddings for graphs with billions of nodes and trillions of edges. It was launched in 2019 and is written entirely in Python. This ensures absolute compatibility with common Python data processing libraries, such as NumPy, Pandas, and scikit-learn. All calculations are performed on the CPU, which should play a decisive role in the hardware selection. A lot of memory is mandatory. It should also be noted that PBG can process very performant large graphs, but is not optimized for small graphs, i.e. structures with less than 100.000 nodes.

Facebook extends the ecosystem of its popular Python scientific computing package PyTorch with a very performant Big Graph solution. If you want to know more about PyTorch, you should read this article from us. Here we will show you the most important features and compare it with the industry’s top performer Google Tensorflow.

Fundamental building blocks

PGB provides some basic building blocks to handle the complexity of the graph. The graph partitioning splits the graph into equal parts and can be processed in parallel. PGB also supports multithreading computations. A process is divided into several threads, which run independently, but can access the same memory. In addition to the distribution of tasks, PyTorch BigGraph can also be used intelligently by distributed execution of hardware resources.

PyTorch BigGraph- How does the training work?

The PGB graph processing algorithms can process the graph in parallel using the fundamental building blocks already described. This allows the training mechanisms to run in a distributed manner and thus with high performance.

Once the nodes and edges are partitioned, the training can be performed for one bucket at a time.

The training runs unsupervised on an input graph by reading its edge list. A feature vector is then output for each entity. Here, neighboring entities in the vector space are placed close to each other, while unconnected entities are pushed apart. Thus, the dimensions are iteratively reduced. It is also possible to configure and optimize this calculation using parameters learned during training.

PGB and Machine Learning

The graph structure is a very information-rich and so far unfortunately too much neglected data structure. With tools like PGB the large structure is more and more equalized by high parallelism.

A very interesting concept is the use of PGB in machine learning large graph structures. Here, the graph structures could be used for semantic queries with nodes, edges and properties to represent and store data and could replace a labeled data structure. Through the connections between the nodes certain relations can be derived. By PGB the graph can be processed enormously parallelized. This would allow individual machines to train a model in parallel with different buckets, using a lock server.

In times of Big Data, the graph has become a popular data structure due to its flexible and clear relationship-based structure. Even entire database systems are now designed according to the graph principle. For more on this, read our article on NoSQL databases. Libraries, like PyGraph, allow you to perform fast queries and optimized graph manipulations. With its full Python implementation, it offers you a user-friendly and powerful tool.

What is a graph?

In a graph, objects are represented according to their relationships with each other. The objects are called vertices and the relations are called edges of the graph. An edge always connects exactly two nodes. Graphs are often used to represent traffic networks, entity-relationship diagrams, syntax trees for programming languages, finite automata and proof or decision trees.

PyGraph supports different graph types

Basically, graphs must be differentiated between directed and undirected. If a graph is directed, the edges may only be used in one direction. These edges are also called directed edges. If it is undirected, there are no directional constraints. So each edge is connected to an undirected pair of vertices. In the following figure we have contrasted both categories.

You can use PyGraph regardless of these properties, because both types are supported.

PyGraph supports several algorithms

PyGraph supports the use of many well-known graph operations. For example, searching or traversing a graph, where all nodes of a graph must be visited, can be done in different ways. In the Depth-First Search (DFS) search algorithm, for example, the successors of a successor of the current node are visited first and only then the neighbors of the current node.

The depth of the search can also be limited accordingly. Breadth-First Search (BFS), on the other hand, first visits its own neighboring nodes and only then the successors of the neighboring nodes.

In addition to the algorithm-based search of a graph, other operations can be performed with PyGraph, such as the calculation of minimum spanning trees. This tree describes the best possible path to traverse all available nodes in a weighted graph. In the following figure we have shown you all currently supported algorithms.