Turnkey Blockchain Explorer Implementation

Imagine: you launch a new blockchain or sidechain. Users want to track their transactions, but without an explorer it's impossible. Building such a service from scratch is non-trivial: you need to sync an archive node, index every block, implement fast search by hashes and addresses. We've done this for 8 projects and know all the pitfalls: from reorgs to performance degradation as the chain grows. Once a startup came to us—their explorer based on The Graph crashed under 50 requests per second; we had to rewrite it entirely.

In this article, I'll show how our approach works: an indexer on PostgreSQL, an API on Next.js, and smart contract decoding. You'll learn how to avoid typical mistakes and cut development time. You'll also understand why investing in a custom solution pays off within six months of active operation. We have implemented 8 blockchain explorers for various EVM networks, including a couple of sidechains with custom features.

System Components

Blockchain node (archive)
    |
    ├── RPC/WebSocket (eth_getBlock, eth_getTransaction...)
    |
Indexer (custom or The Graph)
    |   Reads blocks → parses transactions → stores
    |
PostgreSQL + Redis (cache)
    |
REST API / GraphQL
    |
Frontend (Next.js)
    ├── Search (tx hash, address, block)
    ├── Block list
    ├── Transaction details
    ├── Address profile (balance + history)
    └── Smart contract call decoding

For correct operation, an archive node is required. Learn more in Ethereum development docs.

How to Ensure Minimal Response Time for Hash Search?

The key is proper database indexing and caching. We use PostgreSQL with indexes on hash and block_number, and Redis for hot data (recent blocks, popular addresses). A typical mistake is hitting the node on every search. We always interpose a caching layer; otherwise, TTFB can exceed 2 seconds.

Under a load of 100+ requests per second, our solution maintains an average response time below 200 ms. This is achieved through pre-warming the cache and paginating block queries.

Why a Custom Indexer Is Better Than Off-the-Shelf Solutions?

Ready-made indexers (The Graph, Moralis) are convenient for starting out, but impose limitations: dependency on external servers, inability to fine-tune. Comparison:

For production with high load (over 100 requests/sec), we always recommend a custom indexer. It delivers predictable performance and freedom to extend. According to our data, switching from a ready-made to a custom solution reduces infrastructure costs by 30–40%.

How to Index Blocks: Three Steps

1. Node setup and connection. Deploy an archive node (Geth/Nethermind) or use a WebSocket provider. Ensure the node is ready to return full blocks with transactions.
2. Implement the indexer. Using the viem library, subscribe to new blocks and parse them into PostgreSQL. It's important to handle reorgs—store the previous block hash and recheck on fork.
3. Post-processing. After inserting transactions, update address statistics (balances, transaction count) and place hot data into Redis.

import { createPublicClient, webSocket } from 'viem';
import { mainnet } from 'viem/chains';

const client = createPublicClient({
  chain: mainnet,
  transport: webSocket(process.env.ETH_WS_URL)
});

class BlockIndexer {
  async indexBlock(blockNumber: bigint): Promise<void> {
    const block = await client.getBlock({
      blockNumber,
      includeTransactions: true
    });

    await db.transaction(async (trx) => {
      await trx('blocks').insert({
        number: Number(block.number),
        hash: block.hash,
        parent_hash: block.parentHash,
        timestamp: new Date(Number(block.timestamp) * 1000),
        miner: block.miner,
        gas_used: block.gasUsed.toString(),
        gas_limit: block.gasLimit.toString(),
        transaction_count: block.transactions.length,
        base_fee_per_gas: block.baseFeePerGas?.toString() ?? null
      });

      for (const tx of block.transactions) {
        await trx('transactions').insert({
          hash: tx.hash,
          block_number: Number(tx.blockNumber),
          from_address: tx.from.toLowerCase(),
          to_address: tx.to?.toLowerCase() ?? null,
          value: tx.value.toString(),
          gas: tx.gas.toString(),
          gas_price: tx.gasPrice?.toString() ?? null,
          max_fee_per_gas: tx.maxFeePerGas?.toString() ?? null,
          input: tx.input,
          nonce: tx.nonce,
          transaction_index: tx.transactionIndex
        });

        await this.updateAddressStats(trx, tx.from.toLowerCase());
        if (tx.to) await this.updateAddressStats(trx, tx.to.toLowerCase());
      }
    });
  }

  async watchNewBlocks(): Promise<void> {
    const unwatch = client.watchBlocks({
      onBlock: async (block) => {
        await this.indexBlock(block.number);
      },
      onError: (error) => {
        logger.error('Block watch error', error);
      }
    });

    const latestIndexed = await this.getLatestIndexedBlock();
    const currentBlock = await client.getBlockNumber();

    for (let i = latestIndexed + 1n; i <= currentBlock; i++) {
      await this.indexBlock(i);
    }
  }
}

API: Search

app.get('/api/search', async (req, res) => {
  const query = req.query.q as string;

  if (!query) return res.status(400).json({ error: 'Query required' });

  if (/^0x[0-9a-f]{64}$/i.test(query)) {
    const tx = await db('transactions').where('hash', query.toLowerCase()).first();
    if (tx) return res.json({ type: 'transaction', data: tx });

    const block = await db('blocks').where('hash', query.toLowerCase()).first();
    if (block) return res.json({ type: 'block', data: block });

  } else if (/^0x[0-9a-f]{40}$/i.test(query)) {
    return res.json({ type: 'address', address: query.toLowerCase() });

  } else if (/^\d+$/.test(query)) {
    const block = await db('blocks').where('number', parseInt(query)).first();
    if (block) return res.json({ type: 'block', data: block });
  }

  res.json({ type: 'not_found' });
});

Frontend: Decoding Input Data

import { decodeFunctionData } from 'viem';

async function decodeTransactionInput(
  input: string,
  contractAddress: string
): Promise<DecodedInput | null> {
  if (input === '0x') return null;

  const abi = await getContractAbi(contractAddress);
  if (!abi) return { raw: input };

  try {
    const decoded = decodeFunctionData({ abi, data: input as `0x${string}` });
    return {
      functionName: decoded.functionName,
      args: decoded.args,
      raw: input
    };
  } catch {
    return { raw: input };
  }
}

What's Included in Explorer Development

Work Process

We divide the project into five stages:

During the analytics stage, we clarify what data you need: only blocks and transactions, or a full picture with internal calls and event logs. Design includes preparing a database schema (usually about 15–20 tables) and an API specification in OpenAPI format.

Approximate Timelines

• MVP explorer (transactions, blocks, addresses) without indexer (via RPC) – 2–3 weeks.
• Full indexer with PostgreSQL + API + Frontend – 6–10 weeks.
• Explorer for a custom EVM network – from 2 months.

Infrastructure costs depend on network size and are discussed individually. Contact us to receive a preliminary estimate for your project.

Typical Mistakes When Building a Blockchain Explorer

• Missing backfill after indexer failure – data is lost. Solution: store the last processed block in the DB and resume from there on restart.
• Ignoring reorgs – blocks can change. Subscribe to block events via WebSocket and check hashes.
• Scanning all blocks via RPC without pagination – node overload. Use batch loading with delays.

If you've encountered any of these issues or want to avoid them from the start, request a consultation—we'll help you design a stable explorer.

What's Next?

A custom indexer is an investment in performance and reliability. No dependence on external services: your data is always under control. Order development—we'll propose an architecture for your network within one business day.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.