How to set max pool size in gocql?
Question content
I want to know how to explicitly set the maximum pool size in gocql
?
What I mean is changing the number of queries running simultaneously.
We see 200ms
average response time in Grafana, but we see 24ms
average read latency in the Cassansra exporter panel. Is it related to pool size?
PS I think this is a function in the Java driver: poolingOptions.setMaxRequestsPerConnection(num)
. What is the equivalent function in gocql?
Workaround
I don't think there is an equivalent in gocql to the Cassandra Java driver's setMaxRequestsPerConnection()
.
gocql defaults to 2 connections per host (NumConns: 2
in cluster.go
), on the server side, Cassandra will accept up to 128 Concurrent requests (native_transport_max_threads: 128<code>
in cassandra.yaml). cheers!
The above is the detailed content of How to set max pool size in gocql?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



The main differences between Node.js and Tomcat are: Runtime: Node.js is based on JavaScript runtime, while Tomcat is a Java Servlet container. I/O model: Node.js uses an asynchronous non-blocking model, while Tomcat is synchronous blocking. Concurrency handling: Node.js handles concurrency through an event loop, while Tomcat uses a thread pool. Application scenarios: Node.js is suitable for real-time, data-intensive and high-concurrency applications, and Tomcat is suitable for traditional Java web applications.

Answer: Using NIO technology you can create a scalable API gateway in Java functions to handle a large number of concurrent requests. Steps: Create NIOChannel, register event handler, accept connection, register data, read and write handler, process request, send response

Yes, Node.js is a backend development language. It is used for back-end development, including handling server-side business logic, managing database connections, and providing APIs.

Yes, Node.js can be used for front-end development, and key advantages include high performance, rich ecosystem, and cross-platform compatibility. Considerations to consider are learning curve, tool support, and small community size.

Concurrency testing and debugging Concurrency testing and debugging in Java concurrent programming are crucial and the following techniques are available: Concurrency testing: Unit testing: Isolate and test a single concurrent task. Integration testing: testing the interaction between multiple concurrent tasks. Load testing: Evaluate an application's performance and scalability under heavy load. Concurrency Debugging: Breakpoints: Pause thread execution and inspect variables or execute code. Logging: Record thread events and status. Stack trace: Identify the source of the exception. Visualization tools: Monitor thread activity and resource usage.

In Go functions, asynchronous error handling uses error channels to asynchronously pass errors from goroutines. The specific steps are as follows: Create an error channel. Start a goroutine to perform operations and send errors asynchronously. Use a select statement to receive errors from the channel. Handle errors asynchronously, such as printing or logging error messages. This approach improves the performance and scalability of concurrent code because error handling does not block the calling thread and execution can be canceled.

Swoole is a concurrency framework based on PHP coroutines, which has the advantages of high concurrency processing capabilities, low resource consumption, and simplified code development. Its main features include: coroutine concurrency, event-driven networks and concurrent data structures. By using the Swoole framework, developers can greatly improve the performance and throughput of web applications to meet the needs of high-concurrency scenarios.

High concurrency in Tomcat leads to performance degradation and stability issues, including thread pool exhaustion, resource contention, deadlocks, and memory leaks. Mitigation measures include: adjusting thread pool settings, optimizing resource usage, monitoring server metrics, performing load testing, and using a load balancer.
