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Build a Query

Query Builder allows you to construct queries against your data to produce results for investigation and further exploration. With Query Builder, enter field names, operators, and/or field values in clauses to construct or modify a query.

Use Query Builder to interact and explore your data.

Accessing Query Builder 

In the left navigation menu, select Query with its bar chart and magnifying glass icon. When the left navigation menu is compact, only the icon appears.

Interacting with Query Builder 

Below is an example of a blank Query Builder.

Clauses 

A query in Honeycomb consists of up to six clauses:

• VISUALIZE - Performs a calculation and displays a corresponding graph over time. Most VISUALIZE queries return a line graph while the HEATMAP visualization shows the distribution of data over time
• WHERE - Filters based on field or attribute parameter(s)
• GROUP BY - Groups fields by field or attribute parameter(s)
• ORDER BY - Sort the results
• LIMIT - Specify a limit on how many results to return
• HAVING - Filter results based on aggregate criteria

Relational Fields 

You can use relational fields in the WHERE and GROUP BY clauses to narrow your query to find traces that contain fields and values within a specific span type. You can identify span types by their prefix.

Prefixes 

Relational field span prefixes follow your trace structure and include:

• root. - Identifies the root span

  A root span is the first span in a trace, and does not have a parent span or a parent.id value.

  Full glossary within a trace. To find a match, any additional filters in your query will search through fields only in the specified root. span.

• parent. - Identifies a direct parent span

  A parent span is a span with a parent.id field and dependent child events.

  Full glossary within a trace. To find a match, any additional filters in your query will search through fields only in the specified parent. span and its immediate child spans

  A child span is a dependent span upon a parent span.

  Full glossary .

• any. - Identifies any span within a trace matching a particular field. This prefix allows you to find multiple spans with a certain field and/or value rather than limit to a single span result. Any additional filters will search for spans that match the specified any. span.

  Note

  Using more than one any. term will match many fields on a single span.

  Using any. in a GROUP BY clause requires specifying a field.

Using Query Builder 

To use Query Builder, enter terms. For examples to try, refer to our Query Examples. As you type, Honeycomb autocomplete prompts with relational field prefixes and selectable query components. Execute the query by selecting Run Query or pressing Shift + Enter on the keyboard.

Using Relational Fields 

When you apply relational fields to your query, you can extend queries to use qualifying relationships that indicate where and how spans appear in a trace.

For example, if you managed an e-commerce platform and were trying to determine how many users received an error when they applied a discount code during checkout, you would be interested in finding any applicable spans in your trace.

This example query uses both the root. and any. prefixes to find the count of all spans where an error exists, where any span in the same trace is named getDiscounts and where its root span is named /cart/checkout.

CLAUSE	VALUE	DESCRIPTION
VISUALIZE	COUNT	Calculates count.
WHERE	error exists AND any.name = getDiscounts AND root.name = /cart/checkout	Applies conditions to narrow your query: An error exists (primary condition) The span is in the same trace and is named getDiscounts (secondary condition) The span is contained within a root span named /cart/checkout/ (secondary condition)
GROUP BY	error	Groups by the error.

After querying, you might see the following trace summary when you select a span from the query results:

To learn more about how to use relational fields with specific span types, visit Query Reference: Relational Fields.

Query Builder Results 

The default output for most queries will include:

1. a visualization, or a graph over time
2. a summary table

The precise composition depends on what your query contains.

• Specifying a VISUALIZE clause causes a visualization to be drawn, representing that calculated value over time
• Multiple VISUALIZE clauses result in multiple graphs, one for each calculation.
• Specifying a GROUP BY clause results in the graph having multiple lines, one for each group. The summary table will contain a single row for each unique group.
• Leaving VISUALIZE blank results in raw event data being returned without any summarization.

Editing Queries in Query Builder 

Click on any box in the Query Builder to edit the clauses there. Selecting the X removes the field from the clause. In the image below, the user already entered VISUALIZE COUNT and GROUP BY hostname. When editing, they add a new WHERE clause on app.status_code. Honeycomb autocomplete helps construct the query.

Change Datasets 

In the upper left corner above the Query Builder, use the Dataset Switcher to choose to query a specific Dataset or all Datasets within your Environment. The latter option may be useful if you need to reference events that span across multiple services.

When you switch datasets, Honeycomb will load the existing query on that dataset, but it will not run it automatically.

Change and Compare Query Time Ranges 

In the bottom right corner below the Query Builder, use the time picker to modify the selected time span of the data in your query. Use a preset time range or a custom time range.

Honeycomb supports running queries across different periods of time, so you can compare results to see how your systems change.

Run a Time Comparison query by selecting a time range under Compare to previous time range in the time picker. Honeycomb then will run a query as defined in the Query Builder and another query for the time comparison range selected.

Time comparison queries are currently only in the Query Builder UI and are not supported in the API.

Query Assistant 

Query Assistant is an experimental feature that generates Honeycomb queries based on your natural language query (NLQ) input.

Use Query Assistant to:

• learn how to query in Honeycomb faster
• start with a query and then further refine to explore your data
• onboard new team members to Honeycomb
• explore an unfamiliar dataset

Using Query Assistant 

You can access Query Assistant underneath the Query Builder. To use Query Assistant, enter your prompt in the search box and select Get Query, or select one of the suggested questions below the search box. Based on your entry or selection, Query Assistant creates and runs a query in the Query Builder. Results appear after the screen refreshes.

Viewing Query Assistant 

You can expand and collapse the Query Assistant display. Any changes you make will persist. You can also control your team’s ability to use Query Assistant in Team Settings. To learn how to enable or disable Query Assistant, visit [Teams: Manage Behavior](/get-started/configure/teams/manage-behavior#manage-query-assistant}.

Best Practices 

Query Assistant uses machine learning systems, such as a Large Language Model (LLM) with a generative pre-trained transformer, to assist in creating Honeycomb Queries using natural language.

In addition to your schema, Query Assistant uses the following as context:

• The current query, if it exists
• Suggested Queries for your dataset
• Dataset Definitions

When modifying the current query, Query Assistant translates your prompts into additional query clauses. For example, when given a query that displays overall latency and the prompt “only show errors”, Query Assistant usually adds a WHERE clause to return spans with an error field present and set to true.

When a dataset has Suggested Queries configured, Query Assistant analyzes its fields and generates better results. We recommend configuring your own Suggested Queries for datasets that do not conform to common standards defined by OpenTelemetry instrumentation.

Query Assistant uses the fields defined in Dataset Definitions. For example, OpenTelemetry (and Honeycomb, by default) recognizes any error as a boolean value in the error field. When a Dataset Definition overrides this default with a string value in the app.error field, Query Assistant uses the app.error field instead of the error field when it evaluates prompts.

Limitations 

A user can use up to 50 natural language queries per 24 hours.

Query Assistant is not available to any Honeycomb customer who has signed a HIPAA Business Associate Agreement (BAA).

Data Use 

Honeycomb uses OpenAI’s API for Query Assistant. Honeycomb sends information to OpenAI’s API for the purpose of generating a runnable query based on your input. Data is only sent when you execute a natural language query. In addition, Honeycomb does not use any data to train ML models. In the future, we are interested in using data to create more personalized user experiences, but we have no plans to incorporate data itself, and all data is still subject to our Data Retention window.

What Honeycomb sends to OpenAI:

• Your natural language input
• The names of fields in your dataset schema

What Honeycomb does NOT send to OpenAI:

• Identifying information
• The values of data sent to Honeycomb

OpenAI does not train models on data sent via their API. OpenAI does retain all data for a short period of time to monitor for abuse and misuse. Honeycomb does not use their opt-in mechanism for training and has no plans to offer that as an option for users at this time.

OpenAI’s API exposes the base Large Language Model (LLM) that ChatGPT also uses. ChatGPT adds additional layers of machine learning systems suited for a general-purpose chat application and uses a subset of data it receives to further train their systems. The systems that ChatGPT adds on top of the LLM are not part of Honeycomb’s product implementation.

Query Reference 

VISUALIZE 

Honeycomb supports a wide range of calculations to provide insight into your events. When a grouping is provided, calculations occur within each group; otherwise, anything calculated is done so over all matching events.

For example, say you have collected the following events from your web server logs:

Specifying a visualization for a particular attribute (P95(response_time_ms)for example) means to apply the aggregation function (in this case, P95, or taking the 95th percentile) over the values for the attribute (response_time_ms) across all input events.

Defining multiple VISUALIZE clauses is common and can be useful, especially when comparing their outputs to each other. For example, it can be useful to see both the COUNT and the P95(duration) for a set of events to understand whether latency changes follow volume changes.

While most VISUALIZE queries return a line graph, the HEATMAP visualization allows you see the distribution of data in a rich and interactive way. Heatmaps also allow you to use BubbleUp.

VISUALIZE: Basic Case 

Scenario: we want to capture overall statistics for our web server. Given our four-event dataset described above, consider a query which contains:

• Visualize the overall COUNT
• Visualize the AVG of response_time_ms values
• Visualize the P95 of response_time_ms values

These calculations would return statistics across the entirety of our dataset:

VISUALIZE Summary Table 

The VISUALIZE clause returns both a time series graph and a column of values in the result summary table. Note that in the graph, the values shown are aggregated for each interval at the current granularity. Conversely, the values shown in the summary table are calculated across the entire time range for that query.

These results can be a little surprising for some calculations. The P95(duration_ms) across the entire time range may not look quite like the P95 value at any given point of the curve, because there may be spikes and bumps in the underlying data that are hidden in the time intervals.

The summary table for a HEATMAP shows a histogram of values of that field across the full time range.

VISUALIZE Operations 

Most VISUALIZE operations take a single argument, with the exception of COUNT and CONCURRENCY, which take no arguments. Events that do not have a relevant attribute are ignored, and will not be counted in aggregations.

In the chart below, <field_name> refers to any field name while the <numeric_field_name> argument refers to a float or integer field.

Aggregate	Meaning
COUNT	The number of events
COUNT_DISTINCT(<field_name>)	The number of different values
SUM(<numeric_field_name>)	The sum of the field value
AVG(<numeric_field_name>)	The average value of the field
MAX(<numeric_field_name>)	The maximum value of the field
MIN(<numeric_field_name>)	The minimum value of the field
P001(<numeric_field_name>)	The .1-th percentile of the field
P01(<numeric_field_name>)	The 1st percentile of the field
P05(<numeric_field_name>)	The 5th percentile of the field
P10(<numeric_field_name>)	The 10th percentile of the field
P25(<numeric_field_name>)	The 25th percentile of the field
P50(<numeric_field_name>)	The 50th percentile of the field
P75(<numeric_field_name>)	The 75th percentile of the field
P90(<numeric_field_name>)	The 90th percentile of the field
P95(<numeric_field_name>)	The 95th percentile of the field
P99(<numeric_field_name>)	The 99th percentile of the field
P999(<numeric_field_name>)	The 99.9th percentile of the field
HEATMAP(<numeric_field_name>)	A heatmap of the distribution of that field
CONCURRENCY	The concurrency of the current query, for tracing-enabled datasets only. This is a complex operation; please see the detailed explanatory page on it.
RATE_AVG(<numeric_field_name>)	The difference between subsequent field values after applying the RATE_AVG operator. Learn more about the RATE operators.
RATE_SUM(<numeric_field_name>)	The difference between subsequent field values after applying the RATE_SUM operator. Learn more about the RATE operators.
RATE_MAX(<numeric_field_name>)	The difference between subsequent field values after applying the RATE_MAX operator. Learn more about the RATE operators.

WHERE 

The WHERE clause allows you to constrain events by some attribute besides time. For example, you may want to ignore an outlier case or isolate events triggered by a particular actor or circumstance.

For example, say you have collected the following events from your web server logs:

You can define any number of arbitrary constraints based on event values. WHERE clauses work in concert with the specified time range to define the events that are ultimately considered by any GROUP BY or VISUALIZE clauses.

WHERE: Basic Case 

Scenario: we want to understand the frequency of unsuccessful web requests. Given our three-event dataset described above, consider a query which contains:

• VISUALIZE the overall COUNT
• WHERE status_code != 200

The WHERE clause removes the successful event (our /about web request returning a 200) from consideration, and only counts the first and third events towards our VISUALIZE clause:

WHERE: Multiple Clauses 

Scenario: we want to refine our constraints further, to span multiple attributes for each event. Combining WHERE clauses returns events that satisfy either the intersection of all specified WHERE clauses, or the union. Given our three-event dataset described above, consider a query which contains:

• Where uri = "/about"
• AND status_code != 200

As all three events are considered by the WHERE clauses, only the first one satisfies both:

Honeycomb also allows you to look at the union of clauses by setting to an OR.

• WHERE status_code = 500
• OR status_code = 403

WHERE: Relational Fields 

You can use relational fields in your WHERE clause to narrow your query to find traces that contain fields and values within a specific span type. To learn more and explore some examples, visit Relational Fields in this reference.

WHERE Operations 

WHERE operations may take one or more attributes. Events are only counted if they have a relevant attribute, except in the case of the does-not-exist operation.

Operation	Opposite	Arguments	Meaning
=	!=	1	Exact numerical or string match
starts-with	does-not-start-with	1	String start match
exists	does-not-exist	0	Checks for non-null values
>, >=	<, <=	1	numerical comparison
contains	does-not-contain	1	string inclusion: checks whether the attribute matches a substring of the value
in	not-in	1+	list inclusion: checks whether the attribute matches any item in the list

The syntax for the in operator does not use parentheses. For example, request_method in GET,POST

GROUP BY 

Being able to separate a series of events into groups by attribute is a powerful way to compare segments of your dataset against each other.

For example, say you have collected the following events from your web server logs:

You might want to analyze your web traffic in groups based on the uri ("/about" vs “/docs”) or the status_code (500 versus 200). Choosing to group by uri would return two result rows: one representing events in which uri="/about" and another representing events in which uri="/docs". Each of these grouped results rows will be represented by a single line on a graph.

When using GROUP BY for more than one attribute, Honeycomb will consider each unique combination of values as a single group. Here, choosing to group by both uri and status_code will return three groups: /about+500, /about+200, and /docs+200.

Grouping, paired with calculation, can often reveal interesting patterns in your underlying events—grouping by uri, for example, and calculating response time stats will show you the slowest (or fastest) uris.

The GROUP BY dropdown list also has a shortcut to create a derived column.

Grouping and Visualizing: Better Together 

Scenario: we want to examine performance of our web server by endpoint. Given our four-event dataset described above, consider a query which contains:

• Group by uri
• Visualize the overall COUNT
• Visualize the AVG of response_time_ms values

Pairing a Grouping clause with a Visualize clause results in events being grouped by uri; Honeycomb draws one line for each group, and calculates statistics within each group:

This technique is particularly powerful when paired with an Order By and a Limit to return “Top K”-style results.

In this figure, the user has a VISUALIZE COUNT, and GROUP BY eventtype. The two curves, in purple and orange, show the two groups. The popup shows that the user is hovering the trace_span eventtype, which has a count of 405 in that 15-second time range.

When you move your cursor over the results table at the bottom of the page, each row is highlighted in turn. In the image below, the user has highlighted request and sees the orange line highlighted, and the purple line dimmed.

Rollover for heatmaps is slightly different, as described on the Heatmaps page.

GROUP BY: Relational Fields 

You can use relational fields in your GROUP BY clause to separate a series of events into groups. To learn more and explore some examples, visit Relational Fields in this reference.

ORDER BY 

ORDER BY clauses define how rows will be sorted in the results table.

For example, say you have collected the following events from your web server logs:

You can define any number of ORDER BY clauses in a query and they will be respected in the order they are specified.

The ORDER BY clauses available to you for a particular query are influenced by whether any GROUP BY or VISUALIZE clauses are also specified. If none are, you may order by any of the attributes contained in the dataset. However, once a GROUP BY or VISUALIZE clause exists, you may only order by the values generated by those clauses.

ORDER BY: Basic Case 

Scenario: we just want to get a sense of the slowest endpoints in our web server. Given our four-event dataset described above, consider a query which contains:

• Order by response_time_ms in descending (DESC) order
• Limit to 1 result

Remember that when no Visualize clauses are defined, we simply return raw events as the result rows:

ORDER BY as Paired with VISUALIZE and GROUP BY Clauses 

Scenario: we want to capture statistics for our web server and know what we are looking for (long response_time_mss). Given our four-event dataset described above, consider a query which contains:

• VISUALIZE P95(response_time_ms), or the p95 of response_time_ms values
• GROUP BY uri
• ORDER BY P95(response_time_ms) in descending (DESC) order
• LIMIT to the first 2 results

Our GROUP BY and VISUALIZE queries influence what will be returned as result rows (uri and the P95(response_time_ms) for events within each distinct uri group), while the ORDER BY determines the sort order of those results (longest P95(response_time_ms) first) and the LIMIT throws away any results beyond the top 2:

As you can see, any results referencing the event with uri="/404" was excluded from our result set as a result of its relatively low response_time_ms.

This sort of Top K query is particularly valuable when working with high-cardinality data sets, where a GROUP BY clause might split your dataset into a very large number of groups.

LIMIT 

The LIMIT clause provides a maximum number of result rows to return. By default, queries return 100 result rows. The LIMIT clause allows you to specify up to 1000 rows.

HAVING 

The HAVING clause allows you to filter on the results table. This operation is distinct from the WHERE clause, which filters the underlying events. A HAVING filter can help further refine your query results when grouping on a high-cardinality attribute, which can result in many different rows in the result table. It can also work in tandem with an ORDER BY clause. ORDER BY allows you to order the results, and HAVING filters them.

Like ORDER BY, HAVING selects its series from the results table.

For example, consider a query on VISUALIZE COUNT, P95(duration_ms) and GROUP BY endpoint. This query would show how many times each endpoint ran, and how often it did so. The results table from this query might look something like this:

You might want to ignore the rarest entries when they are least likely to be useful. One way to do that is to add a HAVING clause:

The clause HAVING COUNT > 100 will filter to only results with more than 100 hits on them. You can then ORDER BY P95( duration_ms ) to sort the results to find the slowest endpoints.

HAVING works by filtering specifically on aggregations across all results in the selected time range. It currently does not filter time periods that match the criteria. Take a look at the following example. Each event has counts ranging from 0 - 9 across the time range.

You may want to filter specifically on counts greater than 5 in the example and add the clause HAVING COUNT > 5. This will still return all results in the image, since HAVING filters based off the total in the result table where all values are shown to have counts > 5.

HAVING Clause Options 

The HAVING clause always refers to one of the VISUALIZE clauses. It then takes one or more numeric arguments.

Operator	Opposite	Arguments	Meaning
=	!=	1	numerical equality
>, >=	<, <=	1	comparison
in	not-in	1+	existence in a list

The in operator compares where a value is one of a set: COUNT in 10, 20, 30 checks whether the COUNT is precisely one of those values. The syntax for the in operator does not use parentheses.

Relational Fields 

You can use relational fields to narrow your query to find traces that contain fields and values within a specific span type. Span types are identified by prefixes that follow your trace structure.

You can use relational fields in WHERE or GROUP BY clauses in the Honeycomb UI.

Prefix: Root 

The root. prefix identifies the root span

A root span is the first span in a trace, and does not have a parent span or a parent.id value.

Full glossary within a trace. When you use it, additional filters in your query will match only for spans in traces that match the conditions on the root span. You can also query with root. on its own.

If you are looking for spans somewhere in a trace that you know starts with certain fields, use root..

So you might use a query that contains:

• VISUALIZE COUNT
• WHERE name = HGET AND root.name = /cart AND root.duration_ms > 1000

And you might see the following trace summary when you select a span from the query results:

To explore more examples related to using relational fields with the root. prefix, visit Examples: Query for Traces.

Prefix: Parent 

The parent. prefix identifies a direct parent span

A parent span is a span with a parent.id field and dependent child events.

Full glossary within a trace. When you use it, additional filters in your query will search through fields only in the specified parent. span’s immediate child spans

A child span is a dependent span upon a parent span.

Full glossary . For example, if you know a service is being called at a lower level inside a trace, and you want to find one of its direct child spans, use parent..

You can also use parent. with no additional filters to count the child spans of a parent span. When you do so, Honeycomb will find one parent span match per trace.

So you might use a query that contains:

• VISUALIZE P95(duration_ms)
• WHERE name = retriever_client_fetch AND parent.name = fetch_dataset_samples

And you might see the following trace summary when you select a span from the query results:

To explore more examples related to using relational fields with the parent. prefix, visit Examples: Query for Traces.

Prefix: Any 

The any. prefix identifies any span within a trace. When you use it, additional filters in your query will match only for spans in traces that match your any. span conditions.

So you might use a query that contains:

• VISUALIZE COUNT
• WHERE name = getDiscounts AND any.http.host = cartservice:7070 AND any.duration_ms > 100

And you might see the following trace summary when you select a span from the query results:

Example: Using All Relational Fields Prefixes Together 

You can use all of the relational fields prefixes together to perform a more complex query.

So you might use a query that contains:

• VISUALIZE COUNT
• WHERE error exists AND root.name = /cart/checkout AND parent.duration_ms > 200 AND any.name = getDiscounts
• GROUP BY any.k8s.pod.name

And you might see the following trace summary when you select a span from the query results:

To explore more examples related to using relational fields with the any. prefix, visit Examples: Query for Traces.

Troubleshooting 

Refer to Common Issues with Queries.