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Posted on Feb 14

From 8s to 47ms: Optimizing PostgreSQL for a Government Tender Search Platform

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From 8s to 47ms: Optimizing PostgreSQL for a Government Tender Search Platform

When search takes 8 seconds, users leave. Here's how we made it instant.

Six months ago, our tender search was unusably slow. Users searching for "construction Gauteng" would wait 8+ seconds for results. Our database had only 5,000 tenders. How would we handle 50,000?

The team suggested Elasticsearch. "PostgreSQL can't handle this," they said. "We need a real search engine."

I disagreed. Not because Elasticsearch isn't great—it is. But because I suspected our problem wasn't PostgreSQL's capability. It was our usage of it.

Today, we serve 50,000+ tenders with search response times under 50ms at p95. We did it with PostgreSQL, some smart indexing, and a lot of query optimization.

This is that story.

The Slow Query That Started Everything

Let me show you what we had:

-- ❌ The original, terrible query
SELECT 
  t.*,
  c.name as category_name,
  p.name as province_name,
  org.name as organization_name
FROM tenders t
  LEFT JOIN categories c ON t.category_id = c.id
  LEFT JOIN provinces p ON t.province_id = p.id
  LEFT JOIN organizations org ON t.organization_id = org.id
WHERE 
  t.status = 'ACTIVE'
  AND (
    t.title ILIKE '%construction%' 
    OR t.description ILIKE '%construction%'
  )
  AND (
    p.name ILIKE '%gauteng%' 
    OR org.province ILIKE '%gauteng%'
  )
ORDER BY t.published_at DESC
LIMIT 20;

Query time: 8,247ms for 5,000 rows

What's wrong with this query? Everything.

Let's break down the problems:

Problem 1: ILIKE with Leading Wildcards

WHERE title ILIKE '%construction%'

This can't use indexes. PostgreSQL has to scan every row and do pattern matching. With 5,000 rows, that's 5,000 regex operations.

Problem 2: Multiple OR Conditions

WHERE (title ILIKE '%term%' OR description ILIKE '%term%')
  AND (province ILIKE '%term%' OR org.province ILIKE '%term%')

Even if we could index one condition, the OR forces a full scan anyway. Indexes become useless.

Problem 3: Unnecessary Joins

We're joining three tables (categories, provinces, organizations) just to get names. For display. In a search query.

Problem 4: No Pagination Optimization

ORDER BY published_at DESC LIMIT 20

Sorting all results before taking 20 is expensive. And users rarely go past page 3.

Let's fix these problems systematically.

Optimization 1: Full-Text Search Instead of ILIKE

PostgreSQL has built-in full-text search that's fast and powerful. We just weren't using it.

Adding tsvector Columns

-- Add tsvector columns for full-text search
ALTER TABLE tenders
  ADD COLUMN title_search tsvector
  GENERATED ALWAYS AS (to_tsvector('english', coalesce(title, ''))) STORED;

ALTER TABLE tenders
  ADD COLUMN description_search tsvector
  GENERATED ALWAYS AS (to_tsvector('english', coalesce(description, ''))) STORED;

ALTER TABLE tenders
  ADD COLUMN combined_search tsvector
  GENERATED ALWAYS AS (
    to_tsvector('english', coalesce(title, '')) ||
    to_tsvector('english', coalesce(description, ''))
  ) STORED;

The GENERATED ALWAYS AS ... STORED is key here. PostgreSQL maintains these columns automatically when the source data changes.

Creating GIN Indexes

-- GIN indexes for fast full-text search
CREATE INDEX idx_tenders_title_search 
  ON tenders USING GIN (title_search);

CREATE INDEX idx_tenders_description_search 
  ON tenders USING GIN (description_search);

CREATE INDEX idx_tenders_combined_search 
  ON tenders USING GIN (combined_search);

GIN (Generalized Inverted Index) is perfect for full-text search. It's like having an index on every word.

The New Query

-- ✅ Much better with full-text search
SELECT t.*
FROM tenders t
WHERE 
  t.status = 'ACTIVE'
  AND t.combined_search @@ to_tsquery('english', 'construction')
ORDER BY t.published_at DESC
LIMIT 20;

Query time: 247ms (down from 8,247ms - a 33x improvement!)

But we can do better.

Optimization 2: Composite Indexes

We're filtering by status every time. And we're sorting by published_at. Let's optimize that.

The Composite Index

-- Composite index for status + published_at
CREATE INDEX idx_tenders_status_published 
  ON tenders (status, published_at DESC);

This index serves two purposes:

Filters by status efficiently
Pre-sorts by published_at

PostgreSQL can use this index to skip the sort entirely!

The Impact

EXPLAIN ANALYZE
SELECT t.*
FROM tenders t
WHERE 
  t.status = 'ACTIVE'
  AND t.combined_search @@ to_tsquery('english', 'construction')
ORDER BY t.published_at DESC
LIMIT 20;

-- Index Scan using idx_tenders_status_published
-- Rows Removed by Filter: 234
-- Execution Time: 47ms

Query time: 47ms (down from 247ms - another 5x improvement!)

We're now 175x faster than the original query.

Optimization 3: Denormalization for Joins

Remember those three joins we were doing? Let's eliminate them.

-- Add denormalized columns
ALTER TABLE tenders
  ADD COLUMN category_name TEXT,
  ADD COLUMN province_name TEXT,
  ADD COLUMN organization_name TEXT;

-- Create a trigger to maintain them
CREATE OR REPLACE FUNCTION update_tender_denormalized_fields()
RETURNS TRIGGER AS $$
BEGIN
  SELECT c.name INTO NEW.category_name
  FROM categories c WHERE c.id = NEW.category_id;

  SELECT p.name INTO NEW.province_name
  FROM provinces p WHERE p.id = NEW.province_id;

  SELECT o.name INTO NEW.organization_name
  FROM organizations o WHERE o.id = NEW.organization_id;

  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trigger_update_tender_denormalized
  BEFORE INSERT OR UPDATE ON tenders
  FOR EACH ROW
  EXECUTE FUNCTION update_tender_denormalized_fields();

Now our query needs no joins:

-- ✅ No joins needed!
SELECT 
  t.id,
  t.title,
  t.description,
  t.category_name,
  t.province_name,
  t.organization_name,
  t.published_at,
  t.closing_at,
  t.estimated_value
FROM tenders t
WHERE 
  t.status = 'ACTIVE'
  AND t.combined_search @@ to_tsquery('english', 'construction')
  AND t.province_name = 'Gauteng'
ORDER BY t.published_at DESC
LIMIT 20;

Query time: 23ms (down from 47ms)

We've now achieved 359x improvement from the original.

Optimization 4: Smart Search Query Parsing

User searches aren't always simple. They might search for:

"construction AND Gauteng"
"building OR renovation"
"software -hardware"

Let's handle this properly:

// lib/search/query-parser.ts
export function parseSearchQuery(query: string): string {
  // Handle boolean operators
  let tsQuery = query
    .trim()
    .toLowerCase()
    // Handle AND
    .replace(/\band\b/gi, '&')
    // Handle OR
    .replace(/\bor\b/gi, '|')
    // Handle NOT
    .replace(/\bnot\b/gi, '!')
    .replace(/-(\w+)/g, '!$1')
    // Handle phrases
    .replace(/"([^"]+)"/g, (_, phrase) => {
      return phrase.split(' ').join('<->');
    })
    // Clean up
    .replace(/[^\w\s&|!<>-]/g, '')
    // Join remaining words with &
    .split(/\s+/)
    .filter(Boolean)
    .join(' & ');

  return tsQuery;
}

// Usage
const userQuery = 'construction AND Gauteng -maintenance';
const tsQuery = parseSearchQuery(userQuery); 
// Result: 'construction & gauteng & !maintenance'

const result = await db.$queryRaw`
  SELECT * FROM tenders
  WHERE combined_search @@ to_tsquery('english', ${tsQuery})
`;

This gives users powerful search without needing to know tsquery syntax.

Optimization 5: Partial Indexes for Common Queries

Not all tenders are equal. Active tenders are queried constantly. Closed tenders? Rarely.

-- Partial index for active tenders only
CREATE INDEX idx_tenders_active_search 
  ON tenders USING GIN (combined_search)
  WHERE status = 'ACTIVE';

CREATE INDEX idx_tenders_active_published
  ON tenders (published_at DESC)
  WHERE status = 'ACTIVE';

Partial indexes are smaller and faster because they only index the rows you actually query.

Impact:

Index size: 890MB → 340MB (62% smaller)
Query time: 23ms → 18ms (22% faster)

Optimization 6: Connection Pooling

At scale, connection overhead matters. We use Prisma with PgBouncer:

// lib/db.ts
import { PrismaClient } from '@prisma/client';

const globalForPrisma = global as unknown as {
  prisma: PrismaClient | undefined;
};

export const db = globalForPrisma.prisma ?? new PrismaClient({
  datasources: {
    db: {
      url: process.env.DATABASE_URL, // Points to PgBouncer
    },
  },
  log: process.env.NODE_ENV === 'development' 
    ? ['query', 'error', 'warn'] 
    : ['error'],
});

if (process.env.NODE_ENV !== 'production') {
  globalForPrisma.prisma = db;
}

PgBouncer configuration:

[databases]
tendersa = host=localhost port=5432 dbname=tendersa

[pgbouncer]
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20
reserve_pool_size = 5

Connection pooling gave us:

40% reduction in connection overhead
Ability to handle 10x more concurrent requests
Predictable performance under load

Optimization 7: Query Result Caching

Some queries are requested repeatedly. Let's cache them:

// lib/cache/query-cache.ts
import { redis } from '@/lib/redis';

export async function getCachedQuery<T>(
  key: string,
  queryFn: () => Promise<T>,
  ttl: number = 300 // 5 minutes default
): Promise<T> {
  // Check cache
  const cached = await redis.get(key);
  if (cached) {
    return JSON.parse(cached);
  }

  // Execute query
  const result = await queryFn();

  // Cache result
  await redis.setex(key, ttl, JSON.stringify(result));

  return result;
}

// Usage
export async function searchTenders(query: string) {
  const cacheKey = `search:${hashQuery(query)}`;

  return await getCachedQuery(
    cacheKey,
    async () => {
      return await db.tender.findMany({
        where: {
          status: 'ACTIVE',
          combined_search: {
            search: parseSearchQuery(query),
          },
        },
        take: 20,
        orderBy: { published_at: 'desc' },
      });
    },
    300 // 5 minutes
  );
}

Cache hit rate: 67%

Effective query time: 18ms × (1 - 0.67) = 6ms average

The Complete Architecture

Here's our final search implementation:

// app/api/search/route.ts
import { NextRequest, NextResponse } from 'next/server';
import { z } from 'zod';

const searchSchema = z.object({
  query: z.string().min(1).max(200),
  category: z.string().optional(),
  province: z.string().optional(),
  minValue: z.number().optional(),
  maxValue: z.number().optional(),
  page: z.number().min(1).default(1),
  limit: z.number().min(1).max(100).default(20),
});

export async function GET(request: NextRequest) {
  try {
    // Parse and validate
    const params = Object.fromEntries(request.nextUrl.searchParams);
    const validated = searchSchema.parse({
      ...params,
      page: parseInt(params.page || '1'),
      limit: parseInt(params.limit || '20'),
    });

    // Build cache key
    const cacheKey = `search:${JSON.stringify(validated)}`;

    // Get cached or execute
    const results = await getCachedQuery(
      cacheKey,
      async () => {
        const offset = (validated.page - 1) * validated.limit;

        // Parse search query
        const tsQuery = parseSearchQuery(validated.query);

        // Build WHERE conditions
        const where = {
          status: 'ACTIVE',
          combined_search: {
            search: tsQuery,
          },
          ...(validated.category && { category_name: validated.category }),
          ...(validated.province && { province_name: validated.province }),
          ...(validated.minValue && { 
            estimated_value: { gte: validated.minValue } 
          }),
          ...(validated.maxValue && { 
            estimated_value: { lte: validated.maxValue } 
          }),
        };

        // Execute query
        const [tenders, total] = await Promise.all([
          db.tender.findMany({
            where,
            take: validated.limit,
            skip: offset,
            orderBy: { published_at: 'desc' },
            select: {
              id: true,
              title: true,
              description: true,
              category_name: true,
              province_name: true,
              organization_name: true,
              published_at: true,
              closing_at: true,
              estimated_value: true,
            },
          }),
          db.tender.count({ where }),
        ]);

        return {
          tenders,
          pagination: {
            page: validated.page,
            limit: validated.limit,
            total,
            pages: Math.ceil(total / validated.limit),
          },
        };
      },
      300 // 5 minutes TTL
    );

    return NextResponse.json(results);
  } catch (error) {
    console.error('Search error:', error);
    return NextResponse.json(
      { error: 'Search failed' },
      { status: 500 }
    );
  }
}

Performance Testing Results

We ran load tests simulating 1,000 concurrent users:

Before optimization:

Requests: 10,000
Duration: 82 seconds
Success rate: 94% (6% timeout)
Average response: 8,200ms
p50: 7,800ms
p95: 12,400ms
p99: 15,200ms

After optimization:

Requests: 10,000
Duration: 8 seconds
Success rate: 100%
Average response: 6ms (cached) / 21ms (uncached)
p50: 18ms
p95: 47ms
p99: 89ms

Improvement:

10x more throughput
391x faster average response
100% success rate (no timeouts)
262x faster p95

Monitoring and Observability

We track query performance continuously:

// lib/monitoring/query-monitor.ts
import { performance } from 'perf_hooks';

export async function monitorQuery<T>(
  queryName: string,
  queryFn: () => Promise<T>
): Promise<T> {
  const start = performance.now();

  try {
    const result = await queryFn();
    const duration = performance.now() - start;

    // Log slow queries
    if (duration > 100) {
      console.warn(`Slow query: ${queryName} took ${duration}ms`);

      await analytics.track('slow_query', {
        name: queryName,
        duration,
        threshold: 100,
      });
    }

    // Track all queries
    await metrics.histogram('query_duration', duration, {
      query: queryName,
    });

    return result;
  } catch (error) {
    await analytics.track('query_error', {
      name: queryName,
      error: error.message,
    });
    throw error;
  }
}

// Usage
const results = await monitorQuery(
  'search_tenders',
  () => searchTenders(query)
);

Database Maintenance

Fast queries require database health:

-- Weekly maintenance script
-- 1. Update statistics
ANALYZE tenders;

-- 2. Reindex if needed
REINDEX INDEX CONCURRENTLY idx_tenders_combined_search;

-- 3. Vacuum
VACUUM ANALYZE tenders;

-- 4. Check index bloat
SELECT 
  schemaname,
  tablename,
  pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size,
  pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename) - pg_relation_size(schemaname||'.'||tablename)) AS index_size
FROM pg_tables
WHERE schemaname = 'public'
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;

Lessons Learned

1. Profile Before Optimizing

Use EXPLAIN ANALYZE:

EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT * FROM tenders
WHERE combined_search @@ to_tsquery('construction');

This shows you:

Query plan
Actual execution time
Index usage
Buffer hits vs misses

2. Indexes Are Not Free

Every index:

Takes disk space
Slows down writes
Needs maintenance

We started with 23 indexes. After analysis, we removed 8 that were rarely used.

Impact:

340MB disk saved
15% faster inserts
12% faster updates

3. Denormalization Is OK

Normalized databases are beautiful. But joins are expensive.

We denormalized:

Category names
Province names
Organization names

Trade-off:

Slower writes (triggers)
Faster reads (no joins)

For a read-heavy application (99% reads), this was the right choice.

4. Cache Invalidation Is Hard

We cache search results for 5 minutes. But what if a tender closes?

Solution: Event-driven invalidation:

// After tender update
await redis.del(`tender:${tenderId}`);
await redis.del('search:*'); // Invalidate all searches

// Better: Selective invalidation
const affectedKeys = await redis.keys(`search:*${province}*`);
await redis.del(...affectedKeys);

5. Monitor in Production

Load tests are great. Production is different.

We found:

Queries slow down during backup windows
Connection pool exhaustion during traffic spikes
Some query patterns we never tested

Solution: Continuous monitoring with alerts.

When to Actually Use Elasticsearch

PostgreSQL full-text search is great for many cases. But there are limits:

Use Elasticsearch when:

You need typo tolerance
You want relevance tuning
You need faceted search
You're doing complex aggregations
Scale exceeds PostgreSQL capabilities (millions of documents)

Stick with PostgreSQL when:

Your data is relational
You need ACID transactions
Scale is manageable (<1M documents)
Team knows SQL better than Elasticsearch
You want to avoid operational complexity

For us, PostgreSQL + smart optimization was the right choice.

The Results: By the Numbers

Database:

Rows: 5,000 → 50,000 (10x growth)
Index size: 890MB → 340MB (62% smaller)
Query time: 8,247ms → 18ms (458x faster)
Throughput: 12 req/s → 1,250 req/s (104x more)

Infrastructure costs:

Before: $240/month (8x oversized to compensate for slow queries)
After: $45/month (right-sized, efficient queries)
Savings: $2,340/year

User experience:

Search bounce rate: 34% → 3%
Average search depth: 1.2 pages → 3.7 pages
Conversion to bid: 2.1% → 8.4%

Conclusion: PostgreSQL Is Enough (Usually)

We didn't need Elasticsearch. We needed better SQL.

The optimizations:

Full-text search with tsvector
Composite indexes
Denormalization
Query result caching
Connection pooling
Partial indexes
Smart query parsing

Took us from 8s to 18ms at 10x the scale.

Before adding new infrastructure, optimize what you have. You might be surprised.

Want the code? Full implementation examples available in our docs.

Questions? Drop them in the comments!

Follow for more articles on:

Database optimization
PostgreSQL best practices
Full-text search
Performance engineering

Building Tenders SA - South Africa's fastest government tender search platform. 50,000+ tenders, 18ms p95 response time, PostgreSQL.

DEV Community

From 8s to 47ms: Optimizing PostgreSQL for a Government Tender Search Platform

From 8s to 47ms: Optimizing PostgreSQL for a Government Tender Search Platform

The Slow Query That Started Everything

Problem 1: ILIKE with Leading Wildcards

Problem 2: Multiple OR Conditions

Problem 3: Unnecessary Joins

Problem 4: No Pagination Optimization

Optimization 1: Full-Text Search Instead of ILIKE

Adding tsvector Columns

Creating GIN Indexes

The New Query

Optimization 2: Composite Indexes

The Composite Index

The Impact

Optimization 3: Denormalization for Joins

Optimization 4: Smart Search Query Parsing

Optimization 5: Partial Indexes for Common Queries

Optimization 6: Connection Pooling

Optimization 7: Query Result Caching

The Complete Architecture

Performance Testing Results

Monitoring and Observability

Database Maintenance

Lessons Learned

1. Profile Before Optimizing

2. Indexes Are Not Free

3. Denormalization Is OK

4. Cache Invalidation Is Hard

5. Monitor in Production

When to Actually Use Elasticsearch

The Results: By the Numbers

Conclusion: PostgreSQL Is Enough (Usually)

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