Opvizor Log Analyzer Query Guide

Detailed list for VMware ESXi queries

Detailed list for VMware vCSA (vCenter) queries

Loki comes with its own PromQL-inspired language for queries called LogQL. LogQL can be considered a distributed grep that aggregates log sources and using labels and operators for filtering.

There are two types of LogQL queries: log queries which return the contents of log lines, and metric queries which extend log queries and calculates values based on the counts of logs from a log query.

A basic log query consists of two parts: the log stream selector and a filter expression. Due to Loki's design, all LogQL queries are required to contain a log stream selector.

The log stream selector determines how many log streams (unique sources of log content, such as files) will be searched. A more granular log stream selector reduces the number of searched streams to a manageable volume. This means that the labels passed to the log stream selector will affect the relative performance of the query's execution. The filter expression is then used to do a distributed grep over the aggregated logs from the matching log streams.

The log stream selector determines which log streams should be included in your query results. The stream selector is comprised of one or more key-value pairs, where each key is a log label and the value is that label's value.

The log stream selector is written by wrapping the key-value pairs in a pair of curly braces:

{app="mysql",name="mysql-backup"}

In this example, log streams that have a label of app whose value is mysql and a label of name whose value is mysql-backup will be included in the query results. Note that this will match any log stream whose labels at least contain mysql-backup for their name label; if there are multiple streams that contain that label, logs from all of the matching streams will be shown in the results.

The = operator after the label name is a label matching operator. The following label matching operators are supported:

• =: exactly equal.
• !=: not equal.
• =~: regex matches.
• !~: regex does not match.

Examples:

• {name=~"mysql.+"}
• {name!~"mysql.+"}

The same rules that apply for Prometheus Label Selectors apply for Loki log stream selectors.

After writing the log stream selector, the resulting set of logs can be filtered further with a search expression. The search expression can be just text or regex:

• {job="mysql"} |= "error"
• {name="kafka"} |~ "tsdb-ops.*io:2003"
• {instance=~"kafka-[23]",name="kafka"} != kafka.server:type=ReplicaManager

In the previous examples, |=, |~, and != act as filter operators and the following filter operators are supported:

• |=: Log line contains string.
• !=: Log line does not contain string.
• |~: Log line matches regular expression.
• !~: Log line does not match regular expression.

Filter operators can be chained and will sequentially filter down the expression - resulting log lines must satisfy every filter:

{job="mysql"} |= "error" != "timeout"

When using |~ and !~, Go RE2 syntax regex may be used. The matching is case-sensitive by default and can be switched to case-insensitive prefixing the regex with (?i).

LogQL also supports wrapping a log query with functions that allows for counting entries per stream.

Metric queries can be used to calculate things such as the rate of error messages, or the top N log sources with the most amount of logs over the last 3 hours.

LogQL shares the same range vector concept from Prometheus, except the selected range of samples include a value of 1 for each log entry. An aggregation can be applied over the selected range to transform it into an instance vector.

The currently supported functions for operating over are:

• rate: calculate the number of entries per second
• count_over_time: counts the entries for each log stream within the given range.

count_over_time({job="mysql"}[5m])

This example counts all the log lines within the last five minutes for the MySQL job.

rate({job="mysql"} |= "error" != "timeout" [10s] )

This example demonstrates that a fully LogQL query can be wrapped in the aggregation syntax, including filter expressions. This example gets the per-second rate of all non-timeout errors within the last ten seconds for the MySQL job.

It should be noted that the range notation [5m] can be placed at end of the log stream filter or right after the log stream matcher. For example those two syntaxes below are equivalent.

rate({job="mysql"} |= "error" != "timeout" [5m])

rate({job="mysql"}[5m] |= "error" != "timeout")

Like PromQL, LogQL supports a subset of built-in aggregation operators that can be used to aggregate the element of a single vector, resulting in a new vector of fewer elements but with aggregated values:

• sum: Calculate sum over labels
• min: Select minimum over labels
• max: Select maximum over labels
• avg: Calculate the average over labels
• stddev: Calculate the population standard deviation over labels
• stdvar: Calculate the population standard variance over labels
• count: Count number of elements in the vector
• bottomk: Select smallest k elements by sample value
• topk: Select largest k elements by sample value

The aggregation operators can either be used to aggregate over all label values or a set of distinct label values by including a without or a by clause:

<aggr-op>([parameter,] <vector expression>) [without|by (<label list>)]

parameter is only required when using topk and bottomk. topk and bottomk are different from other aggregators in that a subset of the input samples, including the original labels, are returned in the result vector. by and without are only used to group the input vector.

The without cause removes the listed labels from the resulting vector, keeping all others. The by clause does the opposite, dropping labels that are not listed in the clause, even if their label values are identical between all elements of the vector.

Get the top 10 applications by the highest log throughput:

topk(10,sum(rate({region="us-east1"}[5m])) by (name))

Get the count of logs during the last five minutes, grouping by level:

sum(count_over_time({job="mysql"}[5m])) by (level)

Get the rate of HTTP GET requests from NGINX logs:

avg(rate(({job="nginx"} |= "GET")[10s])) by (region)

Arithmetic binary operators The following binary arithmetic operators exist in Loki:

• + (addition)
• - (subtraction)
• * (multiplication)
• / (division)
• % (modulo)
• ^ (power/exponentiation)

Binary arithmetic operators are defined between two literals (scalars), a literal and a vector, and two vectors.

Between two literals, the behavior is obvious: they evaluate to another literal that is the result of the operator applied to both scalar operands (1 + 1 = 2).

Between a vector and a literal, the operator is applied to the value of every data sample in the vector. E.g. if a time series vector is multiplied by 2, the result is another vector in which every sample value of the original vector is multiplied by 2.

Between two vectors, a binary arithmetic operator is applied to each entry in the left-hand side vector and its matching element in the right-hand vector. The result is propagated into the result vector with the grouping labels becoming the output label set. Entries for which no matching entry in the right-hand vector can be found are not part of the result.

Implement a health check with a simple query:

1 + 1

Double the rate of a a log stream's entries:

sum(rate({app="foo"})) * 2

Get proportion of warning logs to error logs for the foo app

sum(rate({app="foo", level="warn"}[1m])) / sum(rate({app="foo", level="error"}[1m]))

Operators on the same precedence level are left-associative (queries substituted with numbers here for simplicity). For example, 2 * 3 % 2 is equivalent to (2 * 3) % 2. However, some operators have different priorities: 1 + 2 / 3 will still be 1 + ( 2 / 3 ). These function identically to mathematical conventions.

These logical/set binary operators are only defined between two vectors:

• and (intersection)
• or (union)
• unless (complement)

vector1 and vector2 results in a vector consisting of the elements of vector1 for which there are elements in vector2 with exactly matching label sets. Other elements are dropped.

vector1 or vector2 results in a vector that contains all original elements (label sets + values) of vector1 and additionally all elements of vector2 which do not have matching label sets in vector1.

vector1 unless vector2 results in a vector consisting of the elements of vector1 for which there are no elements in vector2 with exactly matching label sets. All matching elements in both vectors are dropped.

This contrived query will return the intersection of these queries, effectively rate({app="bar"})

rate({app=~"foo|bar"}[1m]) and rate({app="bar"}[1m])