![]() Transaction search example This example uses the sample data from the Search Tutorial but should work with any format of Apache web access log. To learn more, see Identify and group events into transactions in this manual. For example, an out of memory problem could trigger several database events to be logged, and they can all be grouped together into a transaction. ![]() One common use of a transaction search is to group multiple events into a single meta-event that represents a single physical event. Use the transaction command to define a transaction or override transaction options specified in nf. Any number of data sources can generate transactions over multiple log entries.Ī transaction search is useful for a single observation of any physical event stretching over multiple logged events. A transaction type is a configured transaction, saved as a field and used in conjunction with the transaction command. SP6 has a separate division that also offers Splunk recruitment and the placement of Splunk professionals into direct-hire (FTE) roles for those companies that may require assistance with acquiring their own full-time staff, given the challenge that currently exists in the market today.A transaction is any group of conceptually-related events that spans time, such as a series of events related to the online reservation of a hotel room by a single customer, or a set of events related to a firewall intrusion incident. SP6 is a Splunk consulting firm focused on Splunk professional services including Splunk deployment, ongoing Splunk administration, and Splunk development. This can be especially useful if you need to do entity-level anomaly detection over many entities such as Docker containers, when your entity count exceeds the limits of ITSI’s out-of-the-box Entity Cohesion anomaly detection. The basics of this can be adapted to several common ITSI transaction processing use cases.įor example, you could take a distinct count of sessions per web server, calculate the per-server percentage of the total, and use this to detect load-balancing problems. Using the same entities and numbers from the example above, you get the following results: Customer In the base search configuration, we can then take the appropriate measure at the entity level and sum it at the aggregate level. How do we track the overall error rate when there is a common occurrence of low-volume entities (customers or suppliers)? We can implement a KPI for error rate contribution per entity count errors at the entity level and get a sum at the aggregate level, then divide the entity count by the total across all entities. The following example of such a calculation will make the problem clear, in this case with low transaction volume and high error rate for an individual entity: Customer The reason for this was fairly simple to understand: different customers or suppliers have different transaction volumes. We found that calculating error rates per entity and then averaging them at the aggregate level often led to either excessive alert noise or failure to detect problems. These KPIs can be split by customer and supplier entities. These can indicate problems with application health, with remote dependencies, or with the transaction requests themselves.įor our purposes, we’ll assume you are handling transactions from both customers and suppliers. One of the most important KPIs to track is transaction error rates. ![]() ![]() ITSI transaction processing can involve transactions flowing from multiple sources, in which case you can use Splunk ITSI to drill down to issues with specific transaction endpoints. ![]()
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