惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
J
Java Code Geeks
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 叶小钗
MyScale Blog
MyScale Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
大猫的无限游戏
大猫的无限游戏
博客园 - 三生石上(FineUI控件)
Google DeepMind News
Google DeepMind News
Engineering at Meta
Engineering at Meta
The Cloudflare Blog
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
N
Netflix TechBlog - Medium
MongoDB | Blog
MongoDB | Blog
I
InfoQ
WordPress大学
WordPress大学
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
H
Help Net Security

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis
Monitor Oracle Database with Datadog
Maxim Brown · 2018-06-14 · via Datadog | The Monitor blog
Maxim Brown

Maxim Brown

Technical Content Writer

Oracle Database has long been a mainstay of the business world. Companies use it to handle data sets backing a wide variety of complex applications, including data warehouses or OLTP systems. Oracle Database includes enterprise-friendly features that emphasize scalability, advanced partitioning, and optimized availability of data across a large, potentially disparate infrastructure, as well as real-time backup and recovery tools.

For Oracle, the name of the game is reliability, scalability, and performance; large-scale enterprises need to ensure that widely dispersed users can access information quickly, and that throughput and storage capacities match demand. Keeping an eye on resource utilization and efficiency is vital to identifying performance bottlenecks, so Datadog is pleased to announce that you can now monitor Oracle databases with our new integration. Once you enable the integration, Datadog will collect and visualize key Oracle Database metrics in a customizable out-of-the-box dashboard. You can then build customized alerts with Oracle metrics to notify you of any performance or capacity issues with your database.

Datadog’s out-of-the-box Oracle Database dashboard

Key Oracle metrics to monitor

Datadog’s Oracle integration tracks dozens of metrics so you can monitor key aspects of Oracle Database resource usage and performance, including metrics related to tablespaces, caches and buffers within the system global area (SGA), and disk I/O.

More room at the table

Each Oracle database consists of two or more tablespaces, logical storage units for data that can be spread across any number of physical data files. These include default tablespaces created when a database is initialized. Resource metrics, such as the percentage of a tablespace in use, can reveal whether you need to update your tablespace allocation. This can be particularly important for the undo tablespace, which is vital for multiversion read consistency, and the temporary tablespace, which stores result sets that are too large to fit in memory.

Oracle tablespace metrics

Cache only, please

For each database instance, Oracle allocates a system global area, a collection of memory structures that includes components such as the buffer cache and the shared pool. The former caches data while the latter stores parsed, executable SQL and PL/SQL code to reduce the overhead of re-parsing identical queries. Running requests through memory is magnitudes faster than reading from disk, so fine-tuning Oracle’s various memory structures is important for optimizing resource usage and query latency. Correlating key metrics like the buffer cache hit ratio with long table scans, or the library cache hit ratio with shared pool usage shows you where you might need to tweak resource allocation to ensure that your caches are efficiently storing and serving up frequently used information.

Against the clock

Any time that the database reads data from or writes data to the disk, it creates the possibility for resource contention. If disk access is high, this can mean reduced performance for users. Keeping an eye on time-consuming disk activities, such as the number of full table scans or disk sorts, can help determine if your application is too reliant on disk I/O. Monitoring metrics like SQL service response times or enqueue timeouts can provide additional insight into whether disk activity is having a detrimental effect on performance.

Oracle latency metrics

The Datadog integration provides additional data for an in-depth look at database usage and performance, including metrics related to session activity, database wait time and, for applications using Real Application Clusters, the global cache.

Getting started

If you’re already using Datadog to monitor your infrastructure and applications, you can set up our integration to start graphing and alerting on Oracle metrics today. If you’d like to see how Datadog can help bring visibility to your infrastructure and applications, you can sign up for a free trial.