High Availability (HA)

Understanding high availability

High availability is critical for systems that process time-series data, particularly in financial markets and industrial applications where downtime can have severe consequences. An HA architecture typically aims for "five nines" (99.999%) uptime, allowing for just minutes of downtime per year. This is achieved through redundant components, automated failover mechanisms, and careful system design that eliminates single points of failure.

In the context of time-series databases, HA involves replicating data across multiple nodes and ensuring seamless failover if one node becomes unavailable. For example, QuestDB implements HA through primary-replica configurations where data is continuously synchronized between nodes:

-- Example of checking replica status
SELECT name, status, last_sync_timestamp 
FROM system.replication_status 
WHERE timestamp > dateadd('h', -1, now());

Implementation strategies

Several key strategies enable high availability in modern systems:

Redundancy and replication

Data replication ensures multiple copies exist across different physical locations. In financial systems processing market data, redundant feed handlers maintain synchronized copies of the order book:

-- Query showing order book status across replicas
SELECT timestamp, symbol, bid_px_00, ask_px_00,
    replica_id, sync_status
FROM AAPL_orderbook
WHERE timestamp > dateadd('m', -5, now())
ORDER BY timestamp DESC;

Automated failover

Automated failover mechanisms detect node failures and redirect traffic without manual intervention. This is particularly important for trading systems where microseconds of downtime can result in significant losses.

Load balancing

Distributing workload across multiple nodes not only improves performance but also enhances availability by preventing any single node from becoming overwhelmed:

-- Example of monitoring node workload
SELECT node_id, cpu_usage, memory_usage, 
    active_queries, timestamp
FROM system.node_metrics
WHERE timestamp > dateadd('m', -5, now());

Common use cases

Financial markets

In capital markets, HA is essential for market data systems and trading platforms. Exchange matching engines require redundant systems to maintain continuous market operation:

-- Monitor trade processing across redundant systems
SELECT system_id, count(*) as trade_count,
    avg(processing_time) as avg_latency
FROM trades
WHERE timestamp > dateadd('h', -1, now())
GROUP BY system_id;

Industrial systems

Manufacturing and industrial processes rely on HA for continuous monitoring and control. Sensor data must be reliably collected and stored:

-- Query showing sensor data availability
SELECT date_trunc('hour', timestamp) as hour,
    count(*) as reading_count,
    count(DISTINCT sensor_id) as active_sensors
FROM weather
WHERE timestamp > dateadd('d', -1, now())
GROUP BY hour;

Summary

High availability is fundamental to modern time-series databases and trading systems. Through careful architecture, redundancy, and automated failover mechanisms, HA systems provide the continuous operation required for critical applications in finance and industry. Understanding HA principles is essential for designing robust data systems that can maintain operation through hardware failures, network issues, and other potential disruptions.