Davis DQL examples
These examples illustrate how to build powerful and flexible health dashboards by using DQL to slice and dice all Davis reported problems and events.
Davis problems represent results that originate from the Davis root-cause analysis runs. In Grail, Davis problems and their updates are stored as Grail events.
- Problem example 1
Count the total number of problems in the last 24 hours. - Problem example 2
Count the current number of active problems. - Problem example 3
Chart the number of problems in the last 7 days to identify a trend within your environment stability. - Problem example 4
Identify the top 10 problem-affected entities within your environment. - Problem example 5
Join entity attributes with detected problems and apply a name filter. - Problem example 6
Load the last state of a given problem. - Problem example 7
Load all active problems and exclude all those that are marked as duplicates. - Problem example 8
Calculate the mean time to resolve for problems over time. - Problem example 9
Show a chart of the concurrently open problems over time.
Davis events represent raw events that originate from various anomaly detectors within Dynatrace or within the OneAgent. Examples here are OneAgent-detected CPU saturation events or high garbage collection time events.
- Davis event example 1
Chart the number of process restart events in the last 7 days.
Count the total number of problems in the last 24 hours
- Fetches table
dt.davis.problems
. - Uses the summarize DQL command to get the total number.
- The
event.id
holds the unique problem ID, which is stable across all refreshes and updates that Davis reports for the same problem.
fetch dt.davis.problems, from:now()-24h, to:now()| summarize {problemCount = countDistinct(event.id)}
Query result
415
Count the current number of active problems
- Fetches table
dt.davis.problems
. - Groups the result by the unique
event.id
field, which contains the problem ID. - Filters out all problems that are no longer in state
ACTIVE
. To do this, the DQL commandtakeLast
of the fieldevent.status
receives the last state.
fetch dt.davis.problems| filter event.status == "ACTIVE"| summarize {activeEvents = countDistinct(event.id)}
Query result
15
Chart the number of problems in the last 7 days to identify a trend within your environment stability
- Fetches table dt.davis.problems.
- Counts in a resolution of 6-hour bins.
fetch dt.davis.problems, from:now()-7d| makeTimeseries count(), time:{timestamp}
Query result
start: 22/11/2023, 11:00 end: 29/11/2023, 12:00
6 h
1.000, 4.000, 1.000, null, 1.000, 3.000, null, null, 3.000, 4.000
Identify the top 10 problem-affected entities within your environment
- Fetches table
dt.davis.problems
. - Expands the arrays field containing all affected entity IDs into individual fields.
- Counts all unique problems grouped by the affected entity IDs.
- Sorts by that problem count.
- Returns the top 10 entity IDs.
fetch dt.davis.problems| expand affected_entity_ids| summarize by:{affected_entity_ids}, count = countDistinct(display_id)| sort count, direction:"descending"| limit 10
Query result
HOST-A9449CACDE12B2BF
10
SERVICE-5624DD59D74FF453
5
PROCESS_GROUP_INSTANCE-3184C659684130C7
3
Fetch all problems for a host with the name "myhost"
A join with entity attributes is performed with the goal to filter all problems with a given host name.
- Fetches table
dt.davis.problems
. - Expands the arrays field containing all affected entity IDs into individual fields.
- Does a topology and entity lookup on the
affected_entity_ids
field. - Enriches the resulting records with two entity fields that are prefixed with
host.
:host.id
andhost.name
. - Applies a filter for the host name
myhost
.
fetch dt.davis.problems| expand affected_entity_ids| lookup sourceField:affected_entity_ids, lookupField:id, prefix:"host.", [fetch dt.entity.host | fields id, name = entity.name]| filter host.name == "myhost"| limit 3
Query result
5/31/2023, 1:31:39 PM
HOST-27D70086952122CF
HOST-27D70086952122CF
myhost
P-23054243
Load the last state of a given problem
A join with entity attributes is performed with the goal to filter all problems with a given host name.
- Fetches table
dt.davis.problems
. - Filters by the unique display identifier of the problem.
fetch dt.davis.problems| filter display_id == "P-24051200"
Query result
5/31/2023, 1:31:39 PM
HOST-27D70086952122CF
HOST-27D70086952122CF
myhost
P-23053506
Load all active problems and exclude all those that are marked as duplicates
Fetch all active problems that were not marked as duplicates. Because the duplicate flag appears during the lifecycle of a problem, the update events need to be sorted by timestamp and then summarized by taking the last state of the duplicate and status fields. Only after sorting them by timestamp is it possible to correctly apply the filter.
- Fetches table
dt.davis.problems
. - Filters out problems that are marked as duplicates.
- Filters out problems that were closed already.
fetch dt.davis.problems| filter event.status == "ACTIVE" and not dt.davis.is_duplicate == "true"
Query result
P-230910385
ACTIVE
P-230910385
false
Calculate the mean time to resolve for problems over time
This example shows how to calculate the mean time necessary to resolve all the reported problems by summarizing the delta between start and end of each problem over time.
- Fetches table
dt.davis.problems
. - Flattens the problem fields into the record.
- Filters out all closed problems.
- Filters out all frequent and duplicate problems.
- Converts the values into a time series of averages over time.
fetch dt.davis.problems, from:now()-7d| filter event.status == "CLOSED"| filter dt.davis.is_frequent_event == false and dt.davis.is_duplicate == false and maintenance.is_under_maintenance == false| makeTimeseries `AVG Problem duration in hours` = avg(toLong(resolved_problem_duration)/3600000000000.0), time:event.end
Show a chart of the concurrently open problems over time
This example shows how to query data for a chart of the concurrently open problems over time by filling all the resolution gaps with the spread
command.
- Fetches table
dt.davis.problems
. - Creates a time series out of the problem count.
- Fills the gaps between start and end timestamps of a problem with the correct count by using the
spread
command.
fetch dt.davis.problems| makeTimeseries count = count(), spread: timeframe(from: event.start, to: coalesce(event.end, now()))
Chart the number of CPU saturation and high-memory events in the last 7 days
- Fetches table
dt.davis.events
for the last 7 days. - Counts in a resolution of 60-minute bins.
fetch dt.davis.events, from:now()-7d, to:now()| filter event.kind == "DAVIS_EVENT"| filter event.type == "OSI_HIGH_CPU" or event.type == "OSI_HIGH_MEMORY"| summarize count = count(), by: {`60m interval` = bin(timestamp, 60m)}
Query result
5/25/2023, 3:00 PM
146
5/25/2023, 4:00 PM
312
5/25/2023, 5:00 PM
201