October 2021 Summaries

The Memgraph App Challenge is an online hackathon introduced by Memgraph to encourage the development of graph-related technologies and innovations within its ecosystem. Participants, either solo or in teams, are invited to create projects that leverage Memgraph, with options ranging from web applications and graph algorithms to integration tools and novel concepts not previously mentioned. The challenge, open to a variety of programming languages though Python is recommended, requires participants to submit their code on GitHub under the MIT License and to provide a short video explaining their project. Prizes include $2,000 for first place, $1,000 for second, and $500 for third, alongside special Memgraph Swag Boxes for select participants. The entries will be evaluated based on creativity, feasibility, and complexity, with submissions closing on December 31st.

The Memgraph App Challenge invites participants to contribute to the graph developer community by creating projects that enhance the Memgraph ecosystem. Open to a wide range of ideas, the challenge encourages entrants to develop web applications, Python scripts, Jupyter Notebooks, or integrations with other technologies, focusing on the analysis and utility of graphs. Participants are urged to consider whether their ideas involve graph analysis, extend Memgraph's capabilities, or would be valuable to other developers. The challenge is supported by resources such as the Memgraph Playground and the Discord server, where participants can seek inspiration, ask questions, and stay updated. Registration is required through the official website, and the initiative aims to foster creativity and collaboration within the open-source community.

The blog post by Katarina Supe explores the process of analyzing Twitch streaming data using a backend architecture that employs various technologies, including Memgraph for graph analytics and Docker for containerization. It details the setup of a system that collects data from Twitch using a Python script, stores it in a Kafka cluster, and processes it with Memgraph, which applies algorithms like PageRank and betweenness centrality to determine the popularity and influence of streamers within the network. The backend, built using Flask and GQLAlchemy, queries Memgraph and serves data to a React frontend, which visualizes the Twitch network using D3.js. The post explains the project structure, data importation methods, including the use of CSV files and object graph mapping, and outlines the backend implementation with a focus on building an API to provide various statistics about streamers, games, and teams. The blog is the first part of a series, with subsequent parts focusing on frontend development and integrating Kafka for streaming data visualization.

In Part 2 of a three-part blog series, the focus is on implementing the frontend of a Twitch streaming graph analysis application using React and D3.js. This installment guides readers through setting up a React app to work alongside a Flask backend, using Semantic UI for styling. It includes instructions on configuring a proxy for seamless communication with the backend, Dockerizing the application, and creating custom hooks to enable D3.js interaction for graph visualization. The blog also details fetching and displaying data, such as game statistics and streamer rankings, using API requests, and highlights the use of powerful query modules like PageRank and Betweenness Centrality to enhance data visualization. The author encourages readers to engage with the community and look forward to Part 3, which will cover streaming data integration with Kafka.

In a company-wide hackathon, a team developed a Spotify song recommendation engine using Kafka, Memgraph, and a web application backend, utilizing an open dataset of Spotify playlists. The application leverages a graph data model to store and analyze data, using custom MAGE algorithms to recommend tracks and playlists based on user preferences and trending popularity. Users can interact with a Vue.js interface to create playlists, which dynamically updates recommendations as new songs are added. The backend is powered by a Python Flask application that handles various REST endpoints for playlist and track management. The project demonstrates the potential of Memgraph for building real-time applications and invites users to explore the system through GitHub and community channels.

Hacktoberfest 2021, presented by DigitalOcean, Intel, appwrite, and deepsource, is an annual event celebrating open-source contributions, where participants can earn rewards by making four pull requests to repositories tagged with "hacktoberfest" during October. Memgraph, despite launching Memgraph 2.0, is eager to join the festivities and encourages contributions to its various repositories, including MAGE, Memgraph core, and GQLAlchemy, which cater to different programming languages and technology stacks. Participants can contribute by reading the contribution guidelines, opening issues for new ideas, and submitting pull requests, with support available from the Memgraph engineering team and community via their Discord server. The event emphasizes adhering to Hacktoberfest's values and Code of Conduct, while cautioning participants against invalid or spammy pull requests, which could lead to disqualification.

Memgraph is a versatile graph database platform that enables users to efficiently stream, graph, and build applications. It supports data ingestion from both historical data sources and real-time streaming sources without the need for custom transformation services, thanks to its built-in streaming clients and transformation modules. At its core, Memgraph DB manages concurrency, consistency, and scaling challenges while utilizing Cypher, a declarative graph query language, and offers an API that supports extensions in multiple programming languages, including C, C++, Python, and Rust. Advanced graph algorithms can be executed using MAGE, and the platform's Bolt protocol ensures high-speed communication across different programming languages. Users can interact with data via the mgconsole command-line interface or visualize it through Memgraph Lab, which provides various graph styles and example queries. Memgraph facilitates application development by enabling the creation of real-time dashboards, social networks, user event tracking systems, and recommendation engines, with examples showcasing its use in analyzing Slack interactions and visualizing Reddit data in real-time.

Memgraph 2.0 introduces several key updates, including making its repository public, which involved navigating new legal concepts, and adding support for temporal types such as Date, LocalDateTime, LocalTime, and Duration, although it does not yet include timezone support. The update enhances the procedure API to allow more complex manipulations within Python and C/C++, enabling users to create vertices, delete edges, and modify properties directly from procedures. Additionally, Memgraph has consolidated its Community and Enterprise Editions into a single binary, simplifying the activation of the Enterprise features through license keys and runtime settings. The release also addresses various bug fixes, such as improved index usage with the MERGE clause and better memory management, and encourages user involvement through their Discord server and issue reporting page.

Memgraph has announced the launch of its high-performance, in-memory graph application platform, designed to make graph applications accessible to developers of all levels, from open-source enthusiasts to large enterprises. Initially developed in 2016 to overcome the limitations of existing enterprise-focused graph databases, Memgraph offers a fast, extensible platform that supports popular query languages and integrates seamlessly into existing architectures. With $9.34 million in funding led by Microsoft's venture fund M12, and contributions from other investors, Memgraph aims to democratize the use of graph applications by providing a Business Source License that blends open-source flexibility with enterprise capabilities. The platform is optimized for streaming data, directly consuming from sources like Kafka, enabling developers to enhance their data processes with graph technology. The Memgraph team encourages the developer community to engage with their offerings by joining their community, subscribing to updates, and trying out the platform through various available resources.

During a company-wide hackathon, Memgraph developed a Slack bot designed to analyze interactions within a Slack communication network using streaming data from Kafka and Memgraph's graph analytics capabilities. The bot gathers data from channels it is part of and creates a graph model with nodes representing users, messages, words, and channels, and edges for reactions, posts, and word count. This real-time application architecture leverages Slack's API and a Kafka cluster to feed data into Memgraph, where it updates a knowledge graph, allowing users to interact with the bot via Slack commands to gain insights into their messaging habits and interactions. The project highlights the integration of knowledge graphs and streaming data, demonstrating its potential through visualization in Memgraph Lab and analysis of network connectivity using NetworkX algorithms. The development and internal testing of Slack Influencer showcased the effectiveness of combining knowledge graphs with real-time streaming data, emphasizing its fun and experimental nature during the hackathon.