supabase Node Memory Pressure

A node is under memory pressure, affecting pod scheduling and performance.

Understanding and Resolving Node Memory Pressure in Supabase

Introduction to Supabase

Supabase is an open-source backend-as-a-service platform that provides developers with a suite of tools to build scalable applications. It offers features like authentication, database management, and real-time subscriptions, all built on top of PostgreSQL. Supabase aims to simplify the development process by providing a seamless integration of these services.

Symptom: Node Memory Pressure

In a Supabase environment, you might encounter a Prometheus alert indicating Node Memory Pressure. This alert signifies that a node is experiencing high memory usage, which can impact the scheduling and performance of pods running on that node.

Understanding Node Memory Pressure

Node Memory Pressure is a critical alert that occurs when the memory usage on a node exceeds a certain threshold. This can lead to performance degradation, as the node may struggle to allocate memory for new pods or maintain the performance of existing ones. This alert is crucial for maintaining the health and efficiency of your Supabase deployment.

Why Does This Happen?

There are several reasons why a node might experience memory pressure:

High memory usage by applications running on the node.
Memory leaks in applications or services.
Insufficient memory resources allocated to the node.

Steps to Fix Node Memory Pressure

Addressing node memory pressure involves a combination of optimizing application performance and managing resources effectively. Here are some steps to resolve this issue:

1. Free Up Memory

Identify and terminate processes that are consuming excessive memory. You can use tools like top or htop to monitor memory usage on the node. To kill a process, use the following command:

kill -9 <PID>

Replace <PID> with the process ID of the application consuming excessive memory.

2. Optimize Application Memory Usage

Review your application's memory usage patterns and optimize them to use memory more efficiently. This might involve:

Refactoring code to reduce memory footprint.
Implementing caching strategies to minimize memory usage.
Using memory profiling tools to identify and fix memory leaks.

3. Scale Resources

If memory pressure persists, consider scaling your resources. This can be done by:

Increasing the memory allocation for the node.
Adding more nodes to distribute the load.

For more information on scaling resources in Kubernetes, refer to the Kubernetes Deployment Management guide.

Conclusion

Node Memory Pressure is a common issue in cloud environments like Supabase, but with the right strategies, it can be effectively managed. By freeing up memory, optimizing application performance, and scaling resources, you can ensure that your Supabase deployment remains healthy and performant. For further reading, check out the Prometheus Documentation for more insights into monitoring and alerting.

Master

supabase Node Memory Pressure

debugging in Minutes

— Grab the Ultimate Cheatsheet

(Perfect for DevOps & SREs)

Most-used commands

Real-world configs/examples

Handy troubleshooting shortcuts

Thankyou for your submission

We have sent the cheatsheet on your email!

Oops! Something went wrong while submitting the form.

supabase Node Memory Pressure

Cheatsheet

(Perfect for DevOps & SREs)

Most-used commands

Thankyou for your submission

We have sent the cheatsheet on your email!

Oops! Something went wrong while submitting the form.

MORE ISSUES

supabase Unauthorized API Access

Detected unauthorized access attempts to the API, indicating potential security threats.

supabase Replication Lag

Significant delay in database replication, which may affect data consistency.

supabase Pod Eviction

Pods are being evicted due to resource constraints or node failures.

supabase Service Dependency Failure

A dependent service is failing, affecting the functionality of the primary service.

supabase Service Latency Spike

Sudden increase in service latency, potentially affecting user experience.

supabase Database Deadlock

Detected deadlocks in the database, which may affect transaction processing.

supabase High I/O Wait

Excessive I/O wait times, indicating potential disk or network bottlenecks.

supabase Node Memory Pressure

A node is under memory pressure, affecting pod scheduling and performance.

supabase High Swap Usage

Excessive swap usage, which may degrade system performance.

supabase Service Restart Loop

A service is continuously restarting, indicating potential configuration or resource issues.

supabase API Rate Limit Exceeded

API requests have exceeded the allowed rate limit, potentially affecting service availability.

supabase Configuration Drift

Detected changes in system configuration that deviate from the desired state.

supabase Node Disk Pressure

A node is experiencing disk pressure, which may affect pod scheduling and performance.

supabase Job Failure

Scheduled jobs or tasks have failed to execute successfully.

supabase Service Unavailable

A service is temporarily unavailable, possibly due to overload or misconfiguration.

supabase High Load Average

The system load average is higher than expected, indicating potential resource saturation.

supabase High Network Traffic

Unusually high network traffic, which may indicate a DDoS attack or misconfigured services.

supabase Backup Failure

Scheduled backups have failed, risking data loss in case of system failures.

supabase High Latency

Increased response times for requests, which may impact user experience.

supabase Unauthorized Access Attempts

Multiple failed login attempts detected, indicating potential security threats.

supabase Pod CrashLoopBackOff

A pod is repeatedly crashing and restarting, indicating issues with the application or configuration.

supabase Certificate Expiry

SSL/TLS certificates are nearing expiration, risking secure communication failures.

supabase Node Not Ready

A node in the cluster is not ready, potentially due to resource constraints or failures.

supabase Service Down

A critical service is not responding, possibly due to crashes or network issues.

supabase High Memory Usage

The memory consumption has surpassed the set limit, which may lead to performance degradation.

supabase Disk Space Low

The available disk space is below the acceptable threshold, risking data write failures.

supabase High CPU Usage

The CPU usage has exceeded the defined threshold, indicating potential over-utilization of server resources.

supabase Database Connection Errors

Frequent connection errors to the database, possibly due to network issues or misconfigurations.

supabase High Error Rate

An increased rate of errors in the application, indicating potential bugs or misconfigurations.

supabase Slow Query Response

Queries are taking longer than expected to execute, affecting application performance.

OpenSearch Index Recovery Failure

An index recovery operation has failed, potentially due to resource constraints or configuration issues.

OpenSearch Cluster Node Joined

A new node has joined the cluster, potentially affecting cluster balance.

OpenSearch Index Read-Only Mode

An index has been set to read-only mode due to disk space issues.

OpenSearch Node Heap Dump Generated

A heap dump has been generated, indicating potential memory issues.

OpenSearch Cluster Node Left

A node has unexpectedly left the cluster.

OpenSearch Node Network Latency High

Network latency between nodes is higher than expected, impacting cluster performance.

OpenSearch Cluster Node Disk Full

A node's disk is full, preventing further data operations.

OpenSearch Cluster State Update Failure

The cluster is unable to update its state due to resource constraints or configuration issues.

OpenSearch Node Disk I/O High

Disk I/O operations on a node are consistently high, impacting performance.

OpenSearch Indexing Throughput Low

The rate of indexing operations is lower than expected.

OpenSearch Search Throughput Low

The rate of search operations is lower than expected.

OpenSearch Node JVM Heap Pressure High

The JVM heap pressure on a node is consistently high, indicating potential memory issues.

OpenSearch Cluster Node Count Low

The number of nodes in the cluster is below the expected count.

OpenSearch Snapshot Failure

A snapshot operation has failed, potentially due to storage issues or configuration errors.

OpenSearch Node Disk Watermark Exceeded

Disk usage on a node has exceeded the high watermark threshold.

OpenSearch Index Shard Size Large

One or more index shards have grown larger than the recommended size.

OpenSearch Snapshot Duration High

Snapshot operations are taking longer than expected to complete.

OpenSearch Cluster Rebalance Failure

The cluster is unable to rebalance shards due to resource constraints or configuration issues.

OpenSearch Pending Tasks High

There is a high number of pending tasks in the cluster, indicating potential bottlenecks.

OpenSearch Cluster Shard Allocation Failure

The cluster is unable to allocate shards due to resource constraints or configuration issues.

OpenSearch Search Latency High

Search queries are taking longer than expected to complete.

OpenSearch Indexing Latency High

Indexing operations are taking longer than expected.

OpenSearch Frequent Garbage Collection

Frequent garbage collection events are occurring, impacting performance.

OpenSearch High JVM Heap Usage

The JVM heap usage is consistently high, leading to potential garbage collection issues.

OpenSearch Node Disk Usage High

The disk usage on one or more OpenSearch nodes is above the threshold.

OpenSearch Node Not Reachable

An OpenSearch node is not reachable or has been removed from the cluster.

OpenSearch Cluster Status Red

One or more primary shards are unassigned in the OpenSearch cluster.

OpenSearch Cluster Status Yellow

One or more replica shards are unassigned in the OpenSearch cluster.

OpenSearch High Memory Usage

The memory usage on the OpenSearch nodes is consistently above the threshold.

OpenSearch High CPU Usage

The CPU usage on the OpenSearch nodes is consistently above the threshold.

ClickHouse ClickHouseHighZooKeeperEphemeralNodeCount

The number of ephemeral nodes in ZooKeeper is too high, which can affect stability.

ClickHouse ClickHouseHighZooKeeperWatchCount

The number of watches in ZooKeeper is too high, potentially affecting performance.

ClickHouse ClickHouseHighZooKeeperNodeCount

The number of nodes in ZooKeeper is too high, which can affect performance.

ClickHouse ClickHouseHighZooKeeperSessionCount

The number of ZooKeeper sessions is too high, potentially overloading the ZooKeeper cluster.

ClickHouse ClickHouseHighZooKeeperRequestErrors

A high number of errors are occurring in requests to ZooKeeper, disrupting coordination.

ClickHouse ClickHouseHighZooKeeperRequestLatency

Requests to ZooKeeper are experiencing high latency, affecting distributed operations.

ClickHouse ClickHouseHighBackgroundTaskQueueSize

The background task queue is too large, potentially delaying important maintenance tasks.

ClickHouse ClickHouseHighMutationQueueSize

The mutation queue size is too large, which can delay data updates.

ClickHouse ClickHouseHighCompactionQueueSize

The compaction queue size is too large, indicating delays in data compaction.

ClickHouse ClickHouseHighPartCountInPartition

A partition has too many parts, which can degrade query performance.

ClickHouse ClickHouseHighReplicaQueueSize

The size of the replication queue is too large, which can delay data synchronization.

ClickHouse ClickHouseHighNetworkErrors

A high number of network errors are occurring, which can disrupt data operations.

ClickHouse ClickHouseHighDiskIOWait

Disk I/O wait times are high, indicating potential bottlenecks in disk operations.

ClickHouse ClickHouseInsertFailureRateHigh

A high rate of insert failures is occurring, which can affect data ingestion.

ClickHouse ClickHouseHighNetworkLatency

Network latency is high, affecting communication between ClickHouse nodes or clients.

ClickHouse ClickHouseQueryFailureRateHigh

A high rate of query failures is occurring, indicating potential issues with queries or server stability.

ClickHouse ClickHouseHighReplicaLag

The lag between replicas and the primary server is too high, risking data consistency.

ClickHouse ClickHouseBackgroundMergesFailing

Background merge operations are failing, which can lead to performance issues.

ClickHouse ClickHouseMergeTreePartCountHigh

The number of parts in a MergeTree table is too high, which can degrade performance.

ClickHouse ClickHouseTableNotReplicated

A table that should be replicated is not being replicated correctly.

ClickHouse ClickHouseZooKeeperSessionExpired

The session with ZooKeeper has expired, potentially disrupting distributed operations.

ClickHouse ClickHouseHighWriteLatency

Write operations are experiencing high latency, which can delay data ingestion.

ClickHouse ClickHouseHighReadLatency

Read operations are experiencing high latency, affecting query performance.

ClickHouse ClickHouseReplicaDown

One or more replicas are not reachable, which can affect data redundancy and availability.

ClickHouse ClickHouseZooKeeperConnectionLoss

The ClickHouse server has lost connection to ZooKeeper, affecting distributed coordination.

ClickHouse ClickHouseHighMemoryUsage

The ClickHouse server is using an unusually high amount of memory, which could lead to performance degradation or crashes.

ClickHouse ClickHouseHighCPUUsage

The CPU usage on the ClickHouse server is consistently high, indicating potential performance issues.

ClickHouse ClickHouseTooManyConnections

The number of connections to the ClickHouse server has exceeded the configured limit.

ClickHouse ClickHouseQueryTimeout

Queries are taking too long to execute and are timing out.

ClickHouse ClickHouseDiskSpaceLow

The disk space on the ClickHouse server is running low, which could prevent new data from being written.

ClickHouse ClickHouseReplicaLag

One or more replicas are lagging behind the primary server, which can lead to stale reads.

Cassandra CassandraClusterWideLatencyHigh

High latency observed across the entire cluster, indicating potential systemic issues.

Cassandra CassandraRepairFailures

Failures occurred during repair operations, potentially affecting data consistency.

Cassandra CassandraNodeLoadImbalance

Uneven data distribution across nodes, leading to load imbalance.

Cassandra CassandraTableCompactionHigh

Compaction tasks for a specific table are taking longer than expected.

Cassandra CassandraHintsDeliveryLatencyHigh

Hint delivery is taking longer than expected, indicating potential network or node issues.

Cassandra CassandraBatchLogReplay

Batch log replay is occurring, indicating potential issues with batch operations.

Cassandra CassandraCQLRequestsHigh

A high number of CQL requests are being processed, potentially overloading the node.

Cassandra CassandraThriftRequestsHigh

A high number of Thrift requests are being processed, potentially overloading the node.

Cassandra CassandraReadRepairFailures

Failures occurred during read repair operations.

Backed by

Resources

Contact

Platform

Connect

Deep Sea Tech Inc. — Made with ❤️ in & 🏢

Doctor Droid