USER STORY
From Minutes to Seconds: How Memgraph Elevated Trademo's Supply Chain Data Retrieval
100m
edges effectively handled
10x
reduction in response rate
5 hops
efficient deep-path traversals
User
Trademo
Industry
Global Trade Intelligence
Profile
Managing Multi-Tier Supply Chain Networks
Challenge
Trademo's new solution using a disk-based multi-model database struggled to model and query the complex multi-tier supply chain data structure. With about 10 million nodes and 100 million relationships, efficiently querying this complex network became a challenge as disk access led to slow query speeds.
Solution
Switching to Memgraph's native in-memory graph database allowed Trademo to achieve faster data retrieval and enhanced query performance. This shift significantly reduced the execution time of long supply chain queries from 1 minute to 7 seconds, enabling Trademo to manage large datasets more effectively and improve overall system performance.
Reading time: 5min
About Trademo
Trademo is an industry-leading global trade intelligence, compliance, and visibility platform powered by a global supply chain knowledge graph called “TrademoKG”. It provides businesses an interconnected view of global trade activities to enhance their global procurement, and research capabilities while mitigating supply chain risks.
Impact highlights
Near real-time query performance
Memgraph significantly reduced message processing times and the need for complex lookup tables.
Enhanced data management
Switching from ArangoDB's disk-based multi-model database to Memgraph's native in-memory graph database, Trademo dramatically reduced query execution times from 1 minute to just 7 seconds. This significant speed boost enables Trademo to provide near real-time supply chain insights to their clients.
End-user satisfaction
Memgraph empowered Trademo to efficiently handle their complex supply chain database, comprising approximately 10 million nodes and 100 million relationships. This improved capacity allows for more comprehensive and accurate supply chain analysis.
“Memgraph saved our project in a way - its query performance was really good! We reduced our time of response by ten times.”
Lakshay Chawla, SDE-2 Data at Trademo
Backstory
Trademo set out to build a Supply Chain Visibility & Risk Assessment System. This system aimed to empower stakeholders with multi-tier supply chain visibility and identify risks associated with potential disruptions.
Imagine being able to see every player in your supply chain, from raw material source to final delivery. This level of transparency allows Trademo to assess potential risks associated with disruptions at any point in the chain.
Imagine being able to see every player in your supply chain, from raw material source to final delivery. This level of transparency allows Trademo to assess potential risks associated with disruptions at any point in the chain.
Challenge:
Trademo struggled to handle complex, multi-tier supply chain relationships and faced slow query performance while managing millions of nodes and relationships.
Initially, Trademo used ArangoDB's disk-based multi-model database to construct a multi-tier supply chain database that could generate critical insights regarding supply chain relationships and operations.
However, they encountered two significant challenges:
Hence, disk-based multi-model databases like ArangoDB failed to meet Trademo's performance needs due to slower query speeds when accessing data from disk rather than memory.
Recognizing the need for faster response times, Trademo sought an efficient in-memory graph database to enhance query performance. This led them to Memgraph!
Okay, so how did Trademo use Memgraph to help with these challenges?
However, they encountered two significant challenges:
- Trademo struggled to effectively manage the complex data relationships between various supply chain entities within their existing database.
- With approximately 10 million nodes and 100 million relationships, Trademo faced difficulties in efficiently querying this intricate network of interconnected data.
Hence, disk-based multi-model databases like ArangoDB failed to meet Trademo's performance needs due to slower query speeds when accessing data from disk rather than memory.
Recognizing the need for faster response times, Trademo sought an efficient in-memory graph database to enhance query performance. This led them to Memgraph!
Okay, so how did Trademo use Memgraph to help with these challenges?
Why Memgraph?
Memgraph's high-performance native in-memory graph database provided Trademo just what they needed — faster data retrieval, enhanced query performance, and efficient management of large datasets.
“People don't have time these days. If you come on a platform that is loading & loading to get your supply chain data, it won't be good for the product. So, query performance in Memgraph is a big game changer.“
Lakshay Chawla, SDE-2 Data at Trademo
Other than its in-memory nature, here's what Trademo loved about Memgraph:
- Memgraph Lab
- Streamlines POC creation with pre-built datasets, which eliminates searching for datasets manually.
- Offers a Graph Style Editor for efficient visualization and navigation of complex results.
- Holds potential for Trademo to leverage generative AI for simplified query writing via GraphChat.
- Cypher Query Language
- Cypher, Memgraph's primary query language, eases the adoption of Memgraph's graph database.
- Docker
- Streamlines Memgraph instance setup and management.
- Enables simplified deployment and maintenance of Trademo's graph database.
- Ensures consistency across environments and ease of scaling.
- Data Loading
- Supports direct data loading from various file formats (CSV, JSON) into the graph database using Cypher query language.
- Community Support & Documentation
- Memgraph's extensive documentation, especially valuable for a new technology like graph database, empowers Trademo's development process.
- Active community forums and Discord server provide timely support and additional resources.
Results
Using Memgraph, Trademo was able to:
Overall, Memgraph has enabled Trademo to enhance their data querying capabilities, improve system performance, and handle large-scale data more effectively.
- Leverage in-memory data storage for faster data access and manipulation.
- Slash the execution time of long supply chain queries from 1 minute to 7 seconds.
- Achieve 10 times faster query response times.
- Effectively manage and query large datasets of hundreds of millions of nodes and edges.
Overall, Memgraph has enabled Trademo to enhance their data querying capabilities, improve system performance, and handle large-scale data more effectively.
Convinced Memgraph can help?
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