Orca Protocol stands as Solana's premier decentralized exchange, handling over $500 million in daily trading volume across thousands of token pairs. Orca has established itself as the "Nasdaq for crypto," providing sophisticated automated market makers (AMM) that optimize price discovery and minimize slippage across blue chips, RWAs, memecoins, and stablecoins alike.

As a leading DEX, Orca operates a core technology: CLMM (Concentrated Liquidity Market Maker) for precision-focused trading. This approach ensures traders get the best possible prices and lowest slippage, particularly crucial when dealing with high-frequency trading patterns and large volume swaps that characterize institutional DeFi activity.

However, operating at Solana's breakneck pace while maintaining the data precision required for optimal pricing algorithms created significant infrastructure challenges. Orca needed a data layer that could:

Handle Solana's Transaction Firehose: Solana processes thousands of transactions per second, and Orca needed to index every trading pool, SPL token, swap, liquidity provision, and transaction in real-time without missing critical pricing events.

Execute Complex Financial Calculations: CLMM formulas require intensive mathematical operations on large datasets. These calculations must run continuously to maintain accurate pricing models, demanding both computational power and data retrieval speed.

Serve Sub-Second Analytics: Trading interfaces and internal dashboards require near-instantaneous access to aggregated fee data, price volumes, and market statistics. Any delay could mean missed trading opportunities or inaccurate market representation.

The Orca team needed a persistent, scalable database that could match their performance requirements while providing the reliability and storage capacity required for a mission-critical financial application processing hundreds of millions in daily volume.

The Trade-Off Dilemma: Speed vs. Persistence in DeFi Infrastructure

Orca's infrastructure requirements revealed the classic database dilemma facing high-frequency financial applications: achieving both blazing-fast query performance and persistent, reliable data storage. Their specific challenges included:

Real-Time Blockchain Indexing at Scale: Every Solana block contains multiple Orca-related transactions across hundreds of trading pools. The database had to ingest this constant stream of trading data, including token pair information, swap details, liquidity changes, and transaction metadata, without creating backpressure that could delay critical pricing updates.

Aggregation Overhead: Maintaining thousands of rolling aggregations across time windows introduced significant operational complexity. TimescaleDB’s continuous aggregates automated these rollups, reducing maintenance cost while ensuring real-time accuracy across Orca’s pricing and liquidity data.

Multi-Dimensional Analytics: Orca's platform aggregates fees and price volumes across multiple time windows (1 hour, 4 hour, 1 day) and various token combinations. This required a database that could handle complex aggregation queries across massive datasets without timing out.

High-Availability Requirements: As a critical piece of Solana's DeFi infrastructure, Orca couldn't afford database downtime. Any outage would immediately impact trading and liquidity provision, potentially costing millions in missed volume.

Building the Nasdaq for Crypto

Orca's solution is to build on Tiger, specifically leveraging TimescaleDB's time-series capabilities to handle their blockchain data requirements. The architecture transformation included:

Comprehensive Blockchain Data Model: Orca designed a schema that captures every element of their trading ecosystem:

Trading pools: Every token pair combination with associated liquidity metrics
SPL tokens: Complete registry of all tokens available on their platform
Swaps: Individual transaction records with pricing, volume, and timing data
Liquidity events: Detailed records of liquidity provision and withdrawal
Transaction metadata: Complete audit trail for regulatory compliance and debugging

Time-Series Optimization: By treating all blockchain data as time-series events, Orca leveraged TimescaleDB's automatic partitioning to ensure query performance remained consistent regardless of data volume growth. Historical data is automatically organized into time-based chunks, enabling efficient queries across specific time windows.

Automated Aggregation Pipeline: Using TimescaleDB's continuous aggregates, Orca built real-time rollups for key metrics:

Fee aggregation across time periods and token pairs
Price volume calculations for market analysis

Compression and Storage Optimization: Compression capabilities enabled by Tiger allowed Orca to achieve an impressive 88% compression ratio on their historical trading data, dramatically reducing storage costs while maintaining full queryability across their entire trading history.

The transition to Tiger provided Orca with a unified platform that combined the speed they needed for real-time operations with the persistence required for financial audit trails and historical analysis. This architecture can now scale while providing ACID compliance for mission-critical financial transactions.

Query Revolution: 3-5 Seconds to Sub-500ms at Massive Scale

The migration to Tiger Data's PostgreSQL platform transformed Orca's query performance dramatically, with the most significant improvements coming from a combination of strategic optimization work and the use of read replica sets. Replica nodes allowed Orca to distribute query load efficiently across their high-traffic dashboards and APIs, ensuring consistent performance even under heavy trading activity.

Some of the improvements include:

Dramatic Performance Gains: Orca's most critical queries, particularly those serving their trading interface, improved from 3-5 seconds (often timing out under load) to a consistent 300-500ms response time. This improvement was achieved through a combination of TimescaleDB's native time-series optimizations and targeted query pattern improvements.

Time-Based Query Optimization: The key breakthrough came from adding historical time limits to their continuous aggregate queries. By leveraging TimescaleDB's time-based partitioning, queries could skip irrelevant data chunks and focus only on the specific time ranges needed for each request.

Operational Excellence: The performance improvements enabled Orca to serve their high-volume interface for liquidity providers reliably, even during peak market conditions. Database forking capabilities allowed them to test new continuous aggregates and query patterns against production-scale data without risking their live trading system. All easily accessible through the Tiger UI, enabling one-click forking, high availability, and read replica sets.

Advanced Database Features: Orca leverages several TimescaleDB capabilities that prove essential for their use case:

Continuous aggregates: Real-time materialized views for fee calculations and volume metrics
Automated compression: Maintains high performance while minimizing storage costs
Time-based partitioning: Enables efficient querying across massive historical datasets
Database forking: Supports safe testing of new features against production data

The team's experience with query optimization demonstrates Tiger’s ability to handle complex financial calculations at scale while maintaining the reliability required for mission-critical trading infrastructure.

Performance Without Compromise

Orca's adoption of Tiger Data has enabled them to operate one of Solana's most successful DEXs while maintaining the technical excellence that differentiates them in a competitive market:

Massive Scale Operations: Processing over $500 million in daily trading volume while maintaining sub-500ms query response times demonstrates the platform's ability to handle enterprise-grade financial workloads.

Storage Efficiency: The 88% compression ratio achieved through TimescaleDB significantly reduces operational costs while maintaining full data accessibility for compliance and analytics purposes.

Operational Confidence: The team reports an overall positive experience with the platform, citing its reliability, ease of use, and performance as key factors in their success.

Development Velocity: Database forking capabilities allow Orca's engineers to iterate rapidly on new features and optimizations without risking production stability, accelerating their development cycle.

Looking ahead, Orca is considering upgrading to Tiger Data's enterprise offering to access enhanced support and additional operational features. Their specific requirements include:

Advanced Query Analytics: Detailed metrics on query count, performance, and resource utilization to optimize their database usage patterns
Query History and Debugging: Enhanced logging capabilities to diagnose complex issues like deadlocking scenarios that can occur under high load
Real-time Customer Service: Direct access to database experts for rapid resolution of performance issues

Orca's partnership with Tiger Data exemplifies how leading DeFi protocols can achieve both massive scale and operational excellence. By choosing a database platform designed for time-series workloads, they've built infrastructure that not only meets their current $500M+ daily volume but can scale to support the next phase of DeFi growth.

As the team continues to expand Orca's market presence and prepare for potential enterprise partnerships, Tiger Data provides the reliable, high-performance foundation they need to remain Solana's premier trading destination. Their success demonstrates that with the right infrastructure choices, DeFi platforms can deliver institutional-grade performance while maintaining the decentralized principles that define the ecosystem.

About the author

Noah Hein

Noah Hein is a Senior Product Marketing Engineer at Tiger Data, where he helps developers understand, adopt, and succeed with the fastest PostgreSQL platform for real‑time and analytical workloads. Day‑to‑day, he translates deep technical capabilities—like hypertables, hypercore compression, and continuous aggregates—into clear product narratives and customer stories that drive adoption and growth.

Before joining Tiger Data, Noah spent several years on the “builder” side of the house as both a founding engineer and an educator. He co‑created Latent Space’s three‑week AI Engineering Fundamentals course and has taught hundreds of engineers how to apply LLMs in production. Noah frequently speaks on AI‑data convergence topics; at the first ever AI Engineer Summit he led the “AI Engineering 101” workshop, walking participants through hands‑on projects.

Outside of work, Noah tries to help more people land jobs with his side project JobMosaic. When he’s not crafting launch posts, you’ll find him experimenting with edge‑AI devices, tinkering with homelab Postgres clusters, or giving impromptu botany lessons to anyone who will listen.

Tiger Powers Orca's Leading Solana DEX with High-Performance Time-Series Infrastructure for 500M+ Daily Volume