Graph Node

Graph Node is the component which indexes Subgraphs, and makes the resulting data available to query via a GraphQL API. As such it is central to the indexer stack, and correct operation of Graph Node is crucial to running a successful indexer.

This provides a contextual overview of Graph Node, and some of the more advanced options available to indexers. Detailed documentation and instructions can be found in the Graph Node repository⁠.

Graph Node

Graph Node⁠ is the reference implementation for indexing Subgraphs on The Graph Network, connecting to blockchain clients, indexing Subgraphs and making indexed data available to query.

Graph Node (and the whole indexer stack) can be run on bare metal, or in a cloud environment. This flexibility of the central indexing component is crucial to the robustness of The Graph Protocol. Similarly, Graph Node can be built from source⁠, or indexers can use one of the provided Docker Images⁠.

Database PostgreSQL

The main store for the Graph Node, this is where Subgraph data is stored, as well as metadata about Subgraphs, and Subgraph-agnostic network data such as the block cache, and eth_call cache.

Clienti della rete

Per indicizzare una rete, Graph Node deve accedere a un cliente di rete tramite un’API JSON-RPC compatibile con EVM. Questo RPC può connettersi a un singolo cliente o può essere una configurazione più complessa che bilancia il carico su più clienti.

While some Subgraphs may just require a full node, some may have indexing features which require additional RPC functionality. Specifically Subgraphs which make eth_calls as part of indexing will require an archive node which supports EIP-1898⁠, and Subgraphs with callHandlers, or blockHandlers with a call filter, require trace_filter support (see trace module documentation here⁠).

Network Firehoses - a Firehose is a gRPC service providing an ordered, yet fork-aware, stream of blocks, developed by The Graph’s core developers to better support performant indexing at scale. This is not currently an Indexer requirement, but Indexers are encouraged to familiarise themselves with the technology, ahead of full network support. Learn more about the Firehose here⁠.

Nodi IPFS

Subgraph deployment metadata is stored on the IPFS network. The Graph Node primarily accesses the IPFS node during Subgraph deployment to fetch the Subgraph manifest and all linked files. Network indexers do not need to host their own IPFS node. An IPFS node for the network is hosted at https://ipfs.thegraph.com⁠.

Server di metriche Prometheus

Per consentire il monitoraggio e la creazione di report, Graph Node può opzionalmente registrare le metriche su un server di metriche Prometheus.

Getting started from source

Install prerequisites

• Rust

• PostgreSQL

• IPFS

• Additional Requirements for Ubuntu users - To run a Graph Node on Ubuntu a few additional packages may be needed.

1sudo apt-get install -y clang libpq-dev libssl-dev pkg-config

Setup

1. Start a PostgreSQL database server

1initdb -D .postgres2pg_ctl -D .postgres -l logfile start3createdb graph-node

2. Clone Graph Node⁠ repo and build the source by running cargo build

3. Now that all the dependencies are setup, start the Graph Node:

1cargo run -p graph-node --release -- \2  --postgres-url postgresql://[USERNAME]:[PASSWORD]@localhost:5432/graph-node \3  --ethereum-rpc [NETWORK_NAME]:[URL] \4  --ipfs https://ipfs.thegraph.com

Come iniziare con Kubernetes

A complete Kubernetes example configuration can be found in the indexer repository⁠.

Porti

Quando è in funzione, Graph Node espone le seguenti porte:

Configurazione avanzata del Graph Node

At its simplest, Graph Node can be operated with a single instance of Graph Node, a single PostgreSQL database, an IPFS node, and the network clients as required by the Subgraphs to be indexed.

This setup can be scaled horizontally, by adding multiple Graph Nodes, and multiple databases to support those Graph Nodes. Advanced users may want to take advantage of some of the horizontal scaling capabilities of Graph Node, as well as some of the more advanced configuration options, via the config.toml file and Graph Node’s environment variables.

config.toml

A TOML⁠ configuration file can be used to set more complex configurations than those exposed in the CLI. The location of the file is passed with the —config command line switch.

A minimal config.toml file can be provided; the following file is equivalent to using the —postgres-url command line option:

1[store]2[store.primary]3connection="<.. postgres-url argument ..>"4[deployment]5[[deployment.rule]]6indexers = [ "<.. list of all indexing nodes ..>" ]

Full documentation of config.toml can be found in the Graph Node docs⁠.

Graph Node multipli

Graph Node indexing can scale horizontally, running multiple instances of Graph Node to split indexing and querying across different nodes. This can be done simply by running Graph Nodes configured with a different node_id on startup (e.g. in the Docker Compose file), which can then be used in the config.toml file to specify dedicated query nodes, block ingestors, and splitting Subgraphs across nodes with deployment rules.

Regole di distribuzione

Given multiple Graph Nodes, it is necessary to manage deployment of new Subgraphs so that the same Subgraph isn’t being indexed by two different nodes, which would lead to collisions. This can be done by using deployment rules, which can also specify which shard a Subgraph’s data should be stored in, if database sharding is being used. Deployment rules can match on the Subgraph name and the network that the deployment is indexing in order to make a decision.

Esempio di configurazione della regola di distribuzione:

1[deployment]2[[deployment.rule]]3match = { name = "(vip|important)/.*" }4shard = "vip"5indexers = [ "index_node_vip_0", "index_node_vip_1" ]6[[deployment.rule]]7match = { network = "kovan" }8# No shard, so we use the default shard called 'primary'9indexers = [ "index_node_kovan_0" ]10[[deployment.rule]]11match = { network = [ "xdai", "poa-core" ] }12indexers = [ "index_node_other_0" ]13[[deployment.rule]]14# There's no 'match', so any Subgraph matches15shards = [ "sharda", "shardb" ]16indexers = [17    "index_node_community_0",18    "index_node_community_1",19    "index_node_community_2",20    "index_node_community_3",21    "index_node_community_4",22    "index_node_community_5"23  ]

Read more about deployment rules here⁠.

Nodi di query dedicati

I nodi possono essere configurati per essere esplicitamente nodi di query includendo quanto segue nel file di configurazione:

1[general]2query = "<regular expression>"

Ogni nodo il cui —node-id corrisponde all’espressione regolare sarà impostato per rispondere solo alle query.

Scalabilità del database tramite sharding

Per la maggior parte dei casi d’uso, un singolo database Postgres è sufficiente per supportare un’istanza del graph-node. Quando un’istanza del graph-node supera un singolo database Postgres, è possibile suddividere l’archiviazione dei dati del graph-node su più database Postgres. Tutti i database insieme formano lo store dell’istanza del graph-node. Ogni singolo database è chiamato shard.

Shards can be used to split Subgraph deployments across multiple databases, and can also be used to use replicas to spread query load across databases. This includes configuring the number of available database connections each graph-node should keep in its connection pool for each database, which becomes increasingly important as more Subgraphs are being indexed.

Lo sharding diventa utile quando il database esistente non riesce a reggere il carico che Graph Node gli impone e quando non è più possibile aumentare le dimensioni del database.

Per quanto riguarda la configurazione delle connessioni, iniziare con max_connections in postgresql.conf impostato a 400 (o forse anche a 200) e osservare le metriche di Prometheus store_connection_wait_time_ms e store_connection_checkout_count. Tempi di attesa notevoli (qualsiasi cosa superiore a 5 ms) indicano che le connessioni disponibili sono troppo poche; tempi di attesa elevati possono anche essere causati da un database molto occupato (come un elevato carico della CPU). Tuttavia, se il database sembra altrimenti stabile, tempi di attesa elevati indicano la necessità di aumentare il numero di connessioni. Nella configurazione, il numero di connessioni che ogni istanza del graph-node può utilizzare è un limite massimo e Graph Node non manterrà aperte le connessioni se non ne ha bisogno.

Read more about store configuration here⁠.

Ingestione di blocchi dedicati

If there are multiple nodes configured, it will be necessary to specify one node which is responsible for ingestion of new blocks, so that all configured index nodes aren’t polling the chain head. This is done as part of the chains namespace, specifying the node_id to be used for block ingestion:

1[chains]2ingestor = "block_ingestor_node"

Supporto di più reti

The Graph Protocol is increasing the number of networks supported for indexing rewards, and there exist many Subgraphs indexing unsupported networks which an indexer would like to process. The config.toml file allows for expressive and flexible configuration of:

• Reti multiple
• Fornitori multipli per rete (questo può consentire di suddividere il carico tra i fornitori e di configurare nodi completi e nodi di archivio, con Graph Node che preferisce i fornitori più economici se un determinato carico di lavoro lo consente).
• Ulteriori dettagli sul provider, come le caratteristiche, l’autenticazione e il tipo di provider (per il supporto sperimentale di Firehose)

The [chains] section controls the ethereum providers that graph-node connects to, and where blocks and other metadata for each chain are stored. The following example configures two chains, mainnet and kovan, where blocks for mainnet are stored in the vip shard and blocks for kovan are stored in the primary shard. The mainnet chain can use two different providers, whereas kovan only has one provider.

1[catene]2ingestor = "block_ingestor_node"3[chains.mainnet]4shard = "vip"5provider = [6  { label = "mainnet1", url = "http://..", features = [], headers = { Authorization = "Bearer foo" } },7  { label = "mainnet2", url = "http://..", features = [ "archivio", "tracce" ] } }8]9[catene.kovan]10shard = "primary"11provider = [ { label = "kovan", url = "http://..", features = [] } ]

Read more about provider configuration here⁠.

Variabili d’ambiente

Graph Node supports a range of environment variables which can enable features, or change Graph Node behaviour. These are documented here⁠.

Distribuzione continua

Gli utenti che gestiscono una configurazione di indicizzazione scalare con una configurazione avanzata possono trarre vantaggio dalla gestione dei Graph Node con Kubernetes.

• The indexer repository has an example Kubernetes reference⁠
• Launchpad⁠ is a toolkit for running a Graph Protocol Indexer on Kubernetes maintained by GraphOps. It provides a set of Helm charts and a CLI to manage a Graph Node deployment.

Gestione del Graph Node

Given a running Graph Node (or Graph Nodes!), the challenge is then to manage deployed Subgraphs across those nodes. Graph Node surfaces a range of tools to help with managing Subgraphs.

Logging

Graph Node’s logs can provide useful information for debugging and optimisation of Graph Node and specific Subgraphs. Graph Node supports different log levels via the GRAPH_LOG environment variable, with the following levels: error, warn, info, debug or trace.

In addition setting GRAPH_LOG_QUERY_TIMING to gql provides more details about how GraphQL queries are running (though this will generate a large volume of logs).

Monitoring & alerting

Graph Node fornisce le metriche tramite l’endpoint Prometheus sulla porta 8040. È possibile utilizzare Grafana per visualizzare queste metriche.

The indexer repository provides an example Grafana configuration⁠.

Graphman

graphman is a maintenance tool for Graph Node, helping with diagnosis and resolution of different day-to-day and exceptional tasks.

The graphman command is included in the official containers, and you can docker exec into your graph-node container to run it. It requires a config.toml file.

Full documentation of graphman commands is available in the Graph Node repository. See [/docs/graphman.md] (https://github.com/graphprotocol/graph-node/blob/master/docs/graphman.md⁠) in the Graph Node /docs

Working with Subgraphs

Stato dell’indicizzazione API

Available on port 8030/graphql by default, the indexing status API exposes a range of methods for checking indexing status for different Subgraphs, checking proofs of indexing, inspecting Subgraph features and more.

The full schema is available here⁠.

Prestazioni di indicizzazione

Il processo di indicizzazione si articola in tre parti distinte:

• Recuperare gli eventi di interesse dal provider
• Elaborare gli eventi in ordine con i gestori appropriati (questo può comportare la chiamata alla chain per lo stato e il recupero dei dati dall’archivio)
• Scrivere i dati risultanti nell’archivio

These stages are pipelined (i.e. they can be executed in parallel), but they are dependent on one another. Where Subgraphs are slow to index, the underlying cause will depend on the specific Subgraph.

Cause comuni di lentezza dell’indicizzazione:

• Time taken to find relevant events from the chain (call handlers in particular can be slow, given the reliance on trace_filter)
• Making large numbers of eth_calls as part of handlers
• Una grande quantità di interazioni con l’archivio durante l’esecuzione
• Una grande quantità di dati da salvare nell’archivio
• Un numero elevato di eventi da elaborare
• Tempo di connessione al database lento, per i nodi affollati
• Il fornitore stesso è in ritardo rispetto alla testa della chain
• Lentezza nell’acquisizione di nuove ricevute dal fornitore alla testa della chain

Subgraph indexing metrics can help diagnose the root cause of indexing slowness. In some cases, the problem lies with the Subgraph itself, but in others, improved network providers, reduced database contention and other configuration improvements can markedly improve indexing performance.

Failed Subgraphs

During indexing Subgraphs might fail, if they encounter data that is unexpected, some component not working as expected, or if there is some bug in the event handlers or configuration. There are two general types of failure:

• Guasti deterministici: si tratta di guasti che non possono essere risolti con tentativi di risposta
• Fallimenti non deterministici: potrebbero essere dovuti a problemi con il provider o a qualche errore imprevisto di Graph Node. Quando si verifica un errore non deterministico, Graph Node riprova i gestori che non hanno funzionato, riducendo il tempo a disposizione.

In some cases a failure might be resolvable by the indexer (for example if the error is a result of not having the right kind of provider, adding the required provider will allow indexing to continue). However in others, a change in the Subgraph code is required.

Cache dei blocchi e delle chiamate

Graph Node caches certain data in the store in order to save refetching from the provider. Blocks are cached, as are the results of eth_calls (the latter being cached as of a specific block). This caching can dramatically increase indexing speed during “resyncing” of a slightly altered Subgraph.

However, in some instances, if an Ethereum node has provided incorrect data for some period, that can make its way into the cache, leading to incorrect data or failed Subgraphs. In this case indexers can use graphman to clear the poisoned cache, and then rewind the affected Subgraphs, which will then fetch fresh data from the (hopefully) healthy provider.

Se si sospetta un’incongruenza nella cache a blocchi, come ad esempio un evento di ricezione tx mancante:

1. graphman chain list to find the chain name.
2. graphman chain check-blocks <CHAIN> by-number <NUMBER> will check if the cached block matches the provider, and deletes the block from the cache if it doesn’t.
   1. If there is a difference, it may be safer to truncate the whole cache with graphman chain truncate <CHAIN>.
   2. Se il blocco corrisponde al provider, è possibile eseguire il debug del problema direttamente sul provider.

Problemi ed errori di query

Once a Subgraph has been indexed, indexers can expect to serve queries via the Subgraph’s dedicated query endpoint. If the indexer is hoping to serve significant query volume, a dedicated query node is recommended, and in case of very high query volumes, indexers may want to configure replica shards so that queries don’t impact the indexing process.

Tuttavia, anche con un nodo di query dedicato e le repliche, alcune query possono richiedere molto tempo per essere eseguite e, in alcuni casi, aumentare l’utilizzo della memoria e avere un impatto negativo sul tempo di query per gli altri utenti.

Non esiste una “pallottola d’argento”, ma una serie di strumenti per prevenire, diagnosticare e gestire le query lente.

Caching delle query

Graph Node caches GraphQL queries by default, which can significantly reduce database load. This can be further configured with the GRAPH_QUERY_CACHE_BLOCKS and GRAPH_QUERY_CACHE_MAX_MEM settings - read more here⁠.

Analisi delle query

Problematic queries most often surface in one of two ways. In some cases, users themselves report that a given query is slow. In that case the challenge is to diagnose the reason for the slowness - whether it is a general issue, or specific to that Subgraph or query. And then of course to resolve it, if possible.

In altri casi, il fattore scatenante potrebbe essere l’elevato utilizzo della memoria su un nodo di query, nel qual caso la sfida consiste nell’identificare la query che causa il problema.

Indexers can use qlog⁠ to process and summarize Graph Node’s query logs. GRAPH_LOG_QUERY_TIMING can also be enabled to help identify and debug slow queries.

Con una query lenta, gli indexer hanno alcune opzioni. Naturalmente possono modificare il loro modello di costo, aumentando in modo significativo il costo di invio della query problematica. Questo può portare a una riduzione della frequenza della query. Tuttavia, questo spesso non risolve la causa principale del problema.

Ottimizzazione di tipo account

Le tabelle di database che memorizzano le entità sembrano essere generalmente di due tipi: “tipo transazioni”, in cui le entità, una volta create, non vengono mai aggiornate, cioè memorizzano qualcosa di simile a un elenco di transazioni finanziarie, e “tipo account”, in cui le entità vengono aggiornate molto spesso, cioè memorizzano qualcosa di simile a conti finanziari che vengono modificati ogni volta che viene registrata una transazione. Le tabelle di tipo account sono caratterizzate dal fatto di contenere un gran numero di versioni di entità, ma relativamente poche entità distinte. Spesso, in queste tabelle il numero di entità distinte è pari all’1% del numero totale di righe (versioni di entità)

For account-like tables, graph-node can generate queries that take advantage of details of how Postgres ends up storing data with such a high rate of change, namely that all of the versions for recent blocks are in a small subsection of the overall storage for such a table.

The command graphman stats show <sgdNNNN> shows, for each entity type/table in a deployment, how many distinct entities, and how many entity versions each table contains. That data is based on Postgres-internal estimates, and is therefore necessarily imprecise, and can be off by an order of magnitude. A -1 in the entities column means that Postgres believes that all rows contain a distinct entity.

In general, tables where the number of distinct entities are less than 1% of the total number of rows/entity versions are good candidates for the account-like optimization. When the output of graphman stats show indicates that a table might benefit from this optimization, running graphman stats show <sgdNNN> <table> will perform a full count of the table - that can be slow, but gives a precise measure of the ratio of distinct entities to overall entity versions.

Once a table has been determined to be account-like, running graphman stats account-like <sgdNNN>.<table> will turn on the account-like optimization for queries against that table. The optimization can be turned off again with graphman stats account-like --clear <sgdNNN>.<table> It takes up to 5 minutes for query nodes to notice that the optimization has been turned on or off. After turning the optimization on, it is necessary to verify that the change does not in fact make queries slower for that table. If you have configured Grafana to monitor Postgres, slow queries would show up in pg_stat_activityin large numbers, taking several seconds. In that case, the optimization needs to be turned off again.

For Uniswap-like Subgraphs, the pair and token tables are prime candidates for this optimization, and can have a dramatic effect on database load.

Removing Subgraphs

At some point an indexer might want to remove a given Subgraph. This can be easily done via graphman drop, which deletes a deployment and all it’s indexed data. The deployment can be specified as either a Subgraph name, an IPFS hash Qm.., or the database namespace sgdNNN. Further documentation is available here⁠.