Identifying and Managing Noisy Neighbors in Multi-Tenant PostgreSQL Deployments (On-Premise)

In multi-tenant PostgreSQL environments, multiple tenants (which can be databases or schemas) share the same physical infrastructure. This design maximizes resource utilization and lowers operational costs. However, it can lead to resource contention, particularly when one tenant becomes a "noisy neighbor" by consuming disproportionate resources such as CPU, memory, or I/O. The resulting performance degradation can negatively impact other tenants. Therefore, identifying and managing noisy neighbors is essential to maintain a balanced, stable environment.

Introduction to Multi-Tenant PostgreSQL Environments

Multi-tenancy in PostgreSQL is an effective way to host multiple independent clients or applications within a single PostgreSQL instance. Tenants can be isolated in two main ways:

1. Schemas: Each tenant resides in its own schema within a single database.
2. Databases: Each tenant has its own database but shares the same PostgreSQL instance.

While multi-tenancy reduces operational overhead and hardware costs, the shared nature of the resources (e.g., CPU, memory, disk I/O) can result in resource contention. This is where the term noisy neighbor comes into play: a tenant consuming disproportionate system resources, negatively affecting others.

What Is a Noisy Neighbor?

A noisy neighbor is a tenant whose queries or workloads consume excessive amounts of system resources, such as CPU, memory, or disk I/O, which in turn impacts the performance of other tenants sharing the same infrastructure. These issues can arise from a variety of factors including inefficient queries, poor database configurations, or unexpected workload spikes.

Challenges with Identifying Noisy Neighbors

1. Shared Resources: In multi-tenant systems, all tenants share the same physical resources. Without clear isolation, it’s difficult to track which tenant is causing the resource strain.
2. Lack of Visibility: PostgreSQL doesn't natively isolate tenants like containers or virtual machines do, making it challenging to monitor resource usage by schema or database.
3. Intermittent Load: Noisy neighbors may only cause issues during peak usage times, making it difficult to detect without continuous monitoring.

Some Methods for Identifying Noisy Neighbors in Multi-Tenant PostgreSQL Deployments

Here are several techniques for detecting noisy neighbors in PostgreSQL:

1. Using PostgreSQL's Built-in Views

PostgreSQL offers several system views that allow you to monitor query execution and resource consumption. Key views to monitor include:

• pg_stat_activity: Provides real-time information about active queries and sessions. This is useful for identifying long-running or resource-intensive queries.

 SELECT pid, usename, application_name, client_addr, backend_start, state, query
FROM pg_stat_activity
WHERE state = 'active';        

• pg_stat_statements: This extension tracks query performance metrics such as execution time, I/O usage, and the number of executions. It’s invaluable for spotting inefficient queries.

SELECT dbid, userid, query, calls, total_time, rows, shared_blks_read, shared_blks_written
FROM pg_stat_statements
ORDER BY total_time DESC
LIMIT 10;        

• pg_stat_io: Offers insights into disk I/O statistics, allowing you to track tenants consuming excessive disk resources.

SELECT * FROM pg_stat_bgwriter;        

By analyzing these views, you can pinpoint the queries and sessions that are consuming excessive resources.

2. Tracking CPU and Memory Usage by Tenant

Since PostgreSQL doesn't provide tenant-specific resource accounting, system-level tools can help correlate PostgreSQL backend processes (identified by their PID) with CPU and memory consumption. Tools like top, htop, or ps on Linux can help you track resource usage by PostgreSQL processes.

This allows you to correlate PostgreSQL PIDs with their CPU and memory usage, helping to identify which tenant is consuming disproportionate system resources.

3. I/O Usage Monitoring

To detect tenants that cause excessive disk I/O, you can use tools such as iostat, iotop, or vmstat. PostgreSQL also provides valuable insights:

• Shared Buffers: Monitor how frequently PostgreSQL accesses shared buffers.
• WAL (Write-Ahead Log): Investigate whether one tenant is generating excessive WAL data, which could negatively affect disk I/O performance.

4. Resource Limiting with Cgroups

Although PostgreSQL lacks native support for resource limiting at the tenant level, you can use Linux cgroups to control CPU and memory usage for specific PostgreSQL processes. By assigning tenants to separate cgroups, you can prevent one tenant from overconsuming resources.

5. Optimizing Query Performance

In many cases, noisy neighbors are the result of inefficient queries consuming excessive resources. Here are a few steps to mitigate this:

• EXPLAIN ANALYZE: Use this command to analyze slow queries and identify optimization opportunities.

 EXPLAIN ANALYZE SELECT * FROM large_table WHERE some_column = 'value';        

• Vacuum and Indexing: Ensure regular vacuuming of tables and proper indexing to reduce the need for full table scans, which can be resource-intensive.

By optimizing queries and database configurations for individual tenants, you can significantly reduce their resource consumption.

6. Third-Party Monitoring Tools

For more detailed monitoring and insights into PostgreSQL performance, third-party tools such as pgBadger, Prometheus with PostgreSQL Exporter, or New Relic can be invaluable. These tools can provide granular visibility into query performance, resource usage, and tenant-specific activity. They also allow you to set up automated alerts when resource thresholds are exceeded, enabling proactive management of noisy neighbors.

Best Practices to Prevent Noisy Neighbors:

• Reduce connection count per instance: To restrict the number of connections per PostgreSQL instance (either globally or per tenant) and manage the connection load effectively, you can implement several strategies.
• Connection Pooling: Use connection pooling tools like PgBouncer to manage database connections efficiently. This prevents a single tenant from overwhelming the server with too many concurrent connections.
• Workload Profiling: Regularly profile tenant workloads, queries, and schema designs to optimize performance and ensure that tenants perform well without negatively affecting others.
• Query Timeouts: Implement query timeouts for long-running queries. This ensures that resource-heavy queries don't lock up system resources indefinitely.
• Fair Resource Management: Use tools like Cgroups or pgPool to limit the resource consumption of individual tenants, ensuring a more even distribution of resources.