Coralogix headquartered in San Francisco aims to help software companies avoid getting lost in their log data by automatically figuring out their production problems. All plans support all Coralogix features and it is available free (up to 1GB of log data), with pricing tiers available based on volume of log data and log retention time, as well as via an enterprise pricing plan.
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kdb+
Score9 out of 10
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kdb+ is a time series database from kx headquartered in Palo Alto, California, a division of First Derivatives.
Most Appropriate: Searching for logs, setting alerts on anomalies, multiple container for logs separated by multiple services. Classification of different types of logs. Tracing of requests involving multiple services. Finding the time between different endpoints in the service layer to identify the choke points. Less Appropriate: Duplication of logs can cause an issue for you.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
The database are all file based, very easy to maintain.
Very solid and fast interface to websocket, so you can interface with javascript easily.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The product is easy to use and integrate. The customer support is at a level of its own. There was never a time we needed help with something, and the support wasn't there to provide all the help needed in a very effective and efficient way. Being proactive, suggesting ways to solve and not just answering questions, and providing more knowledge about how the product should be used.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Think of Coralogix as a proactive teammate rather than just a passive tool. Unlike Datadog or New Relic, where you often pay a steep "tax" to index noise just to catch one error, Coralogix analyzes logs in-stream—saving you from surprise bills when containers get noisy. Its automatic clustering spots hidden issues without you writing endless rules, giving you instant clarity where others force you to dig.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
