Introduction to Hyperion

Hyperion is a full history solution for indexing, storing and retrieving Antelope blockchain’s historical data. It was built by EOS RIO to be an enterprise grade, performant and highly scalable Antelope History Solution. Their documentation is excellent and certainly a worthwhile starting point, this Technical How To series will cover some of their same content and add operational nuances from a practical stand point and EOSphere's experience.

Learn more about EOS RIO Hyperion

Components

The Hyperion Full History service is a collection of purpose built EOS RIO software and industry standard applications. The eight primary building blocks are the following:

EOS RIO Hyperion Indexer and API

The Indexer processes data sourced from an Antelope Leap software State-History (SHIP) node and enables it to be indexed in Elasticsearch. The Hyperion Indexer also makes use of the Antelope Binary to JSON conversion functionality using ABI’s called abieos. Deserialisation performance is greatly improved by using abieos C++ code through EOS RIO’s own NPM package node-abieos that provides a Node.js native binding.

The API is the front end for client queries, it responds to V2 or legacy V1 requests and finds data for these responses by directly querying the Elasticsearch cluster.

Antelope Leap Software State-History (SHIP) Node

The State-History plugin is used by nodeos to capture historical data about the blockchain state and store this data in an externally readable flat file format. This readable file is accessed by the Hyperion Indexer.

RabbitMQ

RabbitMQ is an open source message broker that is used by Hyperion to queue messages and transport data during the multiple stages of indexing to Elasticsearch.

Redis

Redis is an in-memory data structure store and is used by Hyperion as a predictive temporary database cache for HTTP API client queries and as a Indexer transaction cache.

Node.js

The Hyperion indexer and API are Node.js applications and of course then use Node.js as an open-sourced back-end JavaScript runtime environment.

PM2

PM2 is a process manager for Node.js and used to launch and run the Hyperion Indexer and API.

Elasticsearch Cluster

Elasticsearch is a search engine based on the Lucene library, it is used by Hyperion to store and retrieve all indexed data in highly performant schema-free JSON document format.

Kibana

Kibana is a component of the Elastic Stack, a dashboard that enables visualising data and simplified operation and insight of an Elasticsearch cluster. All Hyperion Indexed data resides in the Elasticsearch database, Kibana gives a direct view of this data and the health of the Elasticsearch cluster.

Hyperion Topology

The Topology of your Hyperion deployment depends on your history services requirement and the network you intend to index. Whether it’s Public/Private, Mainnet/Testnet or Full/Partial History.

This guide will discuss EOS Mainnet with Full History. Testnets and Private networks generally have far lower topology and resource requirements.

EOS Mainnet

EOSphere originally started with a single server running all Hyperion Software Components except for the EOS State-History Node. However a challenge was discovered in relation to Elasticsearch JVM heap size when the EOS network utilisation grew and our API became well used.

JVM Heap size is the amount of memory allocated to the Java Virtual Machine of an Elasticsearch node, the more heap available the more cache memory available for indexing and search operations. If it’s too low Hyperion Indexing will be slow and search queries will be very latent. If the JVM heap size is more than 32GB (usually lower than this) on an Elasticsearch node, the threshold for compressed ordinary object pointers (OOP) will be exceeded and JVM will stop using compression. This will be exceptionally inefficient in regards to memory management and the node will consume vastly more memory.

The result of the above is the necessity to create a cluster of more than one Elasticsearch node, as the limit is per Elasticsearch node instance. Two nodes with JVM heap of 25GB results in 50GB of cluster wide heap available.

Other benefits to clustering more than one ElasticSearch node are of course more CPU cores for processing and more DISK for the ever expanding Full History storage requirements. Elasticsearch stores indexed data in documents these documents are allocated to shards, these shards are automatically balanced between nodes in a cluster. Other than distributing the DISK utilisation across nodes, each shard is it’s own Lucene index and as such distributes CPU bandwidth utilisation across the cluster as well.

I recommend reading Elasticsearch: The Definitive Guide as an excellent book to help you understand Elasticsearch concepts.

Taking the above into account our current recommended topology for the EOS Mainnet is to logically or physically run the following nodes:

• Load Balancer
  • SSL Offload
  • Usage Policies
• Hyperion Server 1
  • Hyperion API
  • Hyperion Indexer
  • RabbitMQ
  • Redis
  • Node.js
  • PM2
  • Kibana
• Hyperion Server 2
  • Elasticsearch I (25GB JVM Heap)
• Hyperion Server 3
  • Elasticsearch II (25GB JVM Heap)
• Hyperion Server 4
  • Elasticsearch III (25GB JVM Heap)
• State-History
  • Network sync’d nodeos with state_history plugin enabled

Hyperion Hardware

Similar to Hyperion Topology, Hardware choice will vary on your history services requirement.

The recommendations below are for EOS Mainnet with Full History, in relation to what currently works in the EOSphere Public Service Offerings.

EOS Mainnet

• Load Balancer
  • Dealers choice, however HAProxy is a great option
  • High Speed Internet 100Mb/s+
• Hyperion Server 1
  • Modern CPU, 3Ghz+, 8 Cores+
  • 64GB RAM
  • 128GB DISK (Enterprise Grade SSD/NVMe)
  • 1Gb/s+ LAN
• Hyperion Server 2–4
  • Modern CPU, 3Ghz+, 8 Cores+
  • 64GB RAM
  • Enterprise Grade SSD/NVMe
    The current (February 2024) Elasticsearch Database is 24TB, I suggest provisioning 35–40TB across the cluster for Full History service longevity
  • 1Gb/s+ LAN
• State-History
  • Modern CPU, 4Ghz+, 4 Cores
  • 128GB RAM
  • 256GB DISK 1 (Enterprise Grade SSD/NVMe)
  • 16TB DISK 2 (SAS or SATA are OK)

With that introduction you should now have an informed starting point for your Hyperion services journey.