A Quick Guide to Database Server Monitoring

A Quick Guide to Database Server Monitoring

​Databases are a vital component of each system, generally speaking. They’re employed for the storage, retrieval, and modification of crucial corporate data. Naturally, a Database Server Monitoring administrator’s top priorities are the supply, performance, and security of a database system. ​

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There are several advantages to a correctly configured database monitoring routine. As an example, 

1. A proactive strategy is sometimes preferable to a reactive one while monitoring. It’s crucial to identify any cautionary indications before they become significant incidents.
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   The Database Server Monitoring is usually the primary thing people inspect (and blame) when programmed break down or run slowly. By using database monitoring, potential problems may be rapidly identified and fixed. There isn’t any miracle cure.

   There isn’t only one thanks to creating a “monitoring strategy.” This can be because of the very fact that organizations typically employ various databases, each of which exposes various varieties of metrics at various granularities. A key performance indicator for one platform won’t be significant for an additional.
   Consider the following: Most online transaction processing (OLTP) solutions use relational databases.

   Large amounts of slow-moving data are housed in data warehouse systems. Metrics from each of those groups will also be influenced by

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   1. supplier software (e.g. SQL Server vs. Oracle, MongoDB vs. Cassandra, Redshift vs. Greenplum)
   2. In-house versus cloud hosting
   3. If hosted on a cloud (if the database is managed or non-managed [e.g. RDS vs. EC2]),
   4. When on-site (if the hardware is physical or virtualized),
   5. It is not practicable to list every metric for each combination of the aforementioned.

   Therefore, it’s vital to possess one or more broad categories that can be applied to specific circumstances.

Which Database Server Monitoring Metrics Must be Kept an Eye on?

Here may be a list of some general categories. We’ll discuss some different sorts of database metrics that you ought to give some thought to tracking under each category. This is often not a comprehensive list, but we highlight these because when taken as a whole, they depict the database environment in its whole. Any database monitoring plan should include infrastructure. The metrics must include
Percentage of CPU time spent by the database process

Available memory of Database Server Monitoring

Disk space available Disk queue for waiting Use of I Percent memory Network bandwidth for inbound and outbound traffic

If the metrics are above or below the suitable threshold, we recommend relating the figures to database metrics instead. That’s because hardware or network-related events like a full disc or a saturated network may result in poor query t in poor query performance

The availability of Database Server Monitoring is the next factor to look at. That’s because before staring at the other counters, you would like to make sure the database is accessible and available. Additionally, it prevents consumer complaints before you’re attentive to an outage.

Access to the database node(s) via well-known protocols such as Telnet or Ping may be included in the metrics. The database’s URL and port are accessible (e.g. 3306 for MySQL, 5432 for PostgreSQL, etc.). Multi-node clusters’ master node failover events or slave/peer node upgrade events

Throughput

To establish baselines for typical production performance, throughput should be assessed. There are some standard metrics for this category, albeit they’ll differ between Database Server Monitoring platforms.

1. Database endpoints’ waiting time for connections
2. Number of open connections to databases.
3. The number of read requests made or pending
4. Number of received or active insert, update, or delete commands.
5. Average time to complete a read query Typical execution time for insert, update, or delete instructions
6. Primary and secondary node replication lags

Throughput metrics should be gathered throughout various workload times and provided during a defined continuance to determine performance baselines (e.g., per minute). Repeating the collecting procedure numerous times is suggested.

As baselines accumulate over time, they’ll be used to determine alert levels that are tolerable. Any significant deviation from the mean would necessitate an investigation.

The dashboard for an RDS MySQL cluster is displayed in the following image as an illustration, together with some throughput data. Any abrupt spikes or dips from the typically trending readings are quite obvious from here.

Performance

Performance counters, like throughput, will differ between databases and will be provided within an outlined timeframe. We advise setting baselines for these data points because they will point to possible bottlenecks.

The Standard Ones are as Follows:

Sometimes, these may be shown in a plain manner by third-party monitoring program.

1. Number of pending or blocked read or write queries. 

2. Percentage of disk-based storage accesses
3. Timeouts for database locks, num
4. The number of deadlocks
5. Queries that are taking longer than expected.
6. Out-of-date statistics or useless indexes have prompted warnings.
7. The skewed distribution of data in nodes
8. Traces of applications

You should be able to drill down on reported metrics with a strong monitoring tool. As an example, a query plan should be “clickable” to reveal further information about the indexes or joins the query optimizer selected. The monitoring tools that include the Database Server Monitoring package are often the best for performing this type of performance drill down.

Scheduled Activities of Database Server Monitoring

Databases frequently use scheduled “jobs” to try to repeat activities. While some systems employ croon or third-party schedulers, others include built-in task scheduling features, like Microsoft SQL Server or Oracle. Scheduled tasks include, for instance, incremental and total database backups.

Database cleaning, indexing, reviewing and updating statistics, database integrity checks, log rotation, compaction, etc. are samples of database maintenance duties.

1. Such as nightly data exports and loads, archiving, and so on.
2. Of the functions, the success or failure of scheduled tasks must be tracked.
3. Database metrics may be gathered from a range of sources.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.