Beyond the Block Explorer: Designing the GraphLinq Chain Analytics Platform from Scratch
GraphLinq Analytics reimagined from scratch for a scalable and efficient analytics solution for the GraphLinq Chain with future plans to support additional chains.
Welcome to the first post in a series documenting my journey in building the GraphLinq Chain Analytics platform from the ground up. In this series, I'll walk you through the design, development, and challenges of creating a next-generation analytics solution for blockchain data—going beyond traditional blockchain explorers. I'm using Warp to streamline my development process, and you can join me on this journey. Let's dive in!
The Problem: Capturing the Full Blockchain Truth
Building a real-time analytics platform for a blockchain like GraphLinq isn't just about running a node. We need to store every piece of data—blocks, transactions, logs, and traces—while simultaneously running real-time monitoring, fast analytics, and machine learning anomaly detection. A simple node and database can't handle this; you need a production-grade, horizontally scalable data blueprint.
The goal for the GraphLinq Analytics Platform is a battle-tested system that handles full data capture → stream processing → sub-second querying at massive scale.
The Solution: A Battle-Tested, Scalable Architecture
Our architecture is modeled after patterns used by major blockchain telemetry projects, centered around three core layers: Ingestion, Stream Processing, and Storage.
Layer 1: Ingesting Every Event with Kafka
We run full GraphLinq nodes and use custom exporters/parsers to stream new blocks, transactions, and logs. This data isn't written directly to a database. Instead, it's immediately pushed into Redpanda, our Kafka-compatible message bus.
- Why a Message Bus? It acts as a durable, highly scalable buffer. It completely decouples our data sources (the nodes) from our data consumers (the analytics engine). This allows us to reprocess the stream at any time without overwhelming the nodes.
Layer 2: Real-Time Enrichment with Apache Flink
The raw data streaming through Redpanda isn't immediately useful for analytics. This is where Apache Flink (or PyFlink) comes in as our Stream Processing engine.
- Its Job: Flink consumes the raw stream to enrich transactions (e.g., adding token metadata or exchange mappings) and create materialized, aggregated views (like hourly volume or per-address balances) on the fly, pushing the results downstream for dashboards.
Layer 3: Storage for Speed and History
We use a two-pronged storage approach:
- Fast Analytics (Warm): ClickHouse is our high-performance OLAP store. It's purpose-built for sub-second analytic queries over billions of rows, making it perfect for real-time dashboards and wallet activity lookups.
- Immutable Truth (Cold): Raw data messages are written to MinIO (our S3-compatible object storage) in columnar formats like Parquet/Delta. This is our cheap, durable source of historical truth for batch reprocessing and training ML models.
The Initial Victory: Infrastructure is Live!
As of this status report, the foundational infrastructure for the GraphLinq Analytics Platform is 100% complete and operational!
We have successfully stood up 12 required production-ready services on our self-hosted infrastructure, following the documented stack design. The services currently running include:
- Redpanda (Message Bus)
- Apache Flink (Stream Processing)
- ClickHouse (OLAP Analytics)
- MinIO (Object Storage)
- Prometheus + Grafana (Monitoring)
- TimescaleDB (Time-series DB)
- Elasticsearch (Search/Logs)
What's Missing? The Next Critical Steps
While the infrastructure foundation is perfect, the house is currently empty. We identified several critical gaps that will be the focus of the next few development days:
🚨 Critical Priority 1:
- GraphLinq Chain Node: Because the node takes time to sync and should remain online if possible, it was decided not to make this a container in the project. We have a container already running called glq-chain.
- Blockchain Data Exporter: We need the service that connects to the node and pushes blocks/transactions into Redpanda.
🛠️ High Priority 2:
- ClickHouse Schema: We need the actual tables designed to store the structured blockchain data.
- Flink Processing Jobs: We need to write the stream processing logic (the Flink jobs) that transform the raw data.
The infrastructure is ready. Now, we dive into Data Source Integration and Data Pipeline Development to bring the GraphLinq Analytics Platform to life!
