DQL¶

DQL (Debug Query Language) is the core query language of the TrueWatch platform, specifically designed for efficient querying and analysis of time series data, log data, event data, and more. DQL combines the semantic expression of SQL with the syntactic structure of PromQL, aiming to provide users with a flexible and powerful query tool.

This documentation will help you quickly understand the basic syntax and design philosophy of DQL, and demonstrate how to write DQL queries through examples.

Basic Query Structure¶

The basic structure of a DQL query is as follows:

namespace[index]::datasource[:select-clause] [{where-clause}] [time-expr] [group-by-clause] [having-clause] [order-by-clause] [limit-clause] [sorder-by-clause] [slimit-clause] [soffset-clause]

Where index can be omitted; when omitted, the default index is used.

Execution Order¶

The execution order of a DQL query is crucial, as it determines the query's semantics and performance.

1. Data Filtering: Filter data based on namespace::datasource, where-clause, time-expr

   • Determine the data source
   • Apply WHERE conditions to filter raw data rows
   • Apply time range filtering
   • Filter data as early as possible at this stage to improve subsequent processing efficiency

2. Time Aggregation: If time-expr includes rollup, execute rollup logic first

   • Rollup functions preprocess data along the time dimension
   • For Counter type Metrics, rate or increase is typically calculated instead of using raw values directly
   • For Gauge type Metrics, aggregation functions like last, avg may be used

3. Group Aggregation: Execute group-by-clause grouping and perform aggregation functions within the select-clause for each group

   • Group data based on expressions in the BY clause
   • Calculate aggregation functions (sum, count, avg, max, min, etc.) within each group
   • If there is also a time window, a two-dimensional data structure is formed

4. Group Filtering: Execute having-clause to filter aggregated groups

   • The HAVING clause acts on the aggregated results
   • Can filter based on the results of aggregation functions
   • This is a key difference from the WHERE clause

5. Non-Aggregate Functions: Execute non-aggregate functions within the select-clause

   • Process expressions and functions that do not require aggregation
   • Perform further calculations on aggregated results

6. Intra-group Sorting: Execute order-by-clause, limit-clause to sort and paginate data within each group

   • ORDER BY is executed independently within each group
   • LIMIT restricts the number of data rows returned per group

7. Inter-group Sorting: Execute sorder-by-clause, slimit-clause, soffset-clause to sort and paginate groups

   • SORDER BY sorts the groups themselves
   • Requires dimensionality reduction of group results (e.g., using functions like max, avg, last)
   • SLIMIT limits the number of groups returned

Complete Example¶

Let's understand the structure of DQL through a complete example:

M::cpu:(avg(usage) as avg_usage, max(usage) as max_usage) {host =~ 'web-.*', usage > 50} [1h::5m] BY host, env HAVING avg_usage > 60 ORDER BY time DESC LIMIT 100 SORDER BY avg_usage DESC SLIMIT 10

The meaning of this query is:

• Namespace: M (Metrics data)
• Index: production
• Datasource: cpu
• Selected Fields: Average and maximum values of the usage field
• Time Range: Past 1 hour, aggregated in 5-minute intervals
• Filter Conditions: Hostname starts with 'web' and CPU usage is greater than 50%
• Grouping: Grouped by hostname and environment
• Group Filtering: Average CPU usage greater than 60%
• Sorting: Sorted by time descending, with a maximum of 100 entries per group
• Inter-group Sorting: Sorted by average usage descending, with a maximum of 10 groups

Namespace (namespace)¶

Namespaces are used to distinguish different types of data, each with its specific query methods and storage strategies. DQL supports querying various business data types:

Index (index)¶

Index is an important mechanism for DQL query optimization, which can be understood as a table or partition in a traditional database. Within a single namespace, the system may split data based on factors such as data source, data volume, and access patterns to improve query performance and management efficiency.

Role of Index¶

• Performance Optimization: Distribute data storage through indexes to reduce the amount of data scanned in a single query
• Data Isolation: Data from different businesses, environments, or time ranges can be stored in different indexes
• Permission Management: Different access permissions can be set for different indexes
• Lifecycle Management: Different data retention policies can be set for different indexes

Naming Rules for Index¶

• Index names can be explicitly declared; when not explicitly declared, default is used by default
• When explicitly declaring an index name, wildcards or regular expressions for matching are not supported
• Index names typically reflect the business attributes of the data, such as: production, staging, web-logs, api-logs

Basic Syntax¶

// Use default index (automatically uses default when not specified)
M::cpu           // Equivalent to M("default")::cpu
L::nginx         // Equivalent to L("default")::nginx

// Specify a single index
M("production")::cpu     // Query CPU Metrics in the production index
L("web-logs")::nginx     // Query Nginx logs in the web-logs index

// Multi-index query (query data from multiple indexes simultaneously)
M("production", "staging")::cpu     // Query CPU Metrics from production and staging environments
L("web-logs", "api-logs")::nginx     // Query Web and API logs

Index and Performance¶

Proper use of indexes can significantly improve query performance:

• Precise Index: Directly specify the index when you know exactly which index the data is in
• Multi-index Query: Use multi-index syntax when querying across multiple indexes instead of using wildcards
• Avoid Full Index Scans: Try to reduce the data scan range through a combination of indexes and WHERE conditions

Compatible Syntax (Not Recommended)¶

Due to historical reasons, DQL also supports specifying indexes in the where clause, but this is not recommended:

// Old syntax, not recommended
L::nginx { index = "web-logs" }
L::nginx { index IN ["web-logs", "api-logs"] }

Application Examples¶

// Query CPU usage in the production environment
M::cpu:(avg(usage)) [1h] BY host

// Compare production and staging environments
M("production", "staging")::cpu:(avg(usage)) [1h] BY index, host

// Analyze web server logs
L("web-logs")::nginx:(count(*)) {status >= 400} [1h] BY status

// Compare error rates between Web and API servers
L("web-logs", "api-logs")::*:(count(*)) {status >= 500} [1h] BY index

Datasource (datasource)¶

The datasource specifies the specific data source for the query, which can be a dataset name, a wildcard pattern, a regular expression, or a subquery.

Basic Datasource¶

The definition of datasource varies across different namespaces:

Datasource Syntax¶

Specify Datasource Name¶

M::cpu:(usage)                    // Query CPU Metrics
L::nginx:(count(*))                // Query Nginx logs
T::user-service:(traces)           // Query user service traces

Wildcard Matching¶

M::*:(usage)                      // Query all Metrics

Regular Expression Matching¶

M::re('cpu.*'):(usage)            // Query Metrics starting with cpu
L::re('web.*'):(count(*))          // Query logs starting with web
T::re('.*-service'):(traces)      // Query services ending with -service

Subquery Datasource¶

Subqueries are an important feature in DQL for implementing complex analysis, allowing the results of one query to be used as the datasource for another. This nested query mechanism supports multi-level analysis requirements.

Execution Mechanism¶

The execution of subqueries follows these principles:

1. Serial Execution: The inner subquery executes first, and its results are then used as the datasource for the outer query
2. Result Encapsulation: Subquery results are encapsulated into a temporary table structure for use by the outer query
3. Namespace Mixing: Subqueries support mixing different namespaces, enabling cross-data type analysis
4. Performance Consideration: Subqueries increase computational complexity; query logic needs to be designed reasonably

Basic Syntax¶

namespace::(subquery):(projections)

Execution Process¶

Take a typical subquery as an example:

L::(L::*:(count(*)) {level = 'error'} BY app_id):(count_distinct(app_id))

The execution process is:

1. Inner Subquery: L::*:(count(*)) {level = 'error'} BY app_id

   • Scan all log data
   • Filter out logs with error level
   • Count the number of errors grouped by app_id
   • Generate temporary table: app_id | count(*)

2. Outer Query: L::(...):(count_distinct(app_id))

   • Use the subquery result as the datasource
   • Count how many distinct app_ids there are
   • Final result: Number of applications with errors

Application Examples¶

// Count the number of applications with errors
L::(L::*:(count(*)) {level = 'error'} BY app_id):(count_distinct(app_id))

// Analyze servers with high CPU usage
M::(M::cpu:(avg(usage)) [1h] BY host {avg(usage) > 80}):(count(host))

// First find service endpoints with error rates exceeding 1%, then count the number of affected services
M::(M::http_requests:(sum(request_count), sum(error_count)) [1h] BY service, endpoint
   {sum(error_count) / sum(request_count) > 0.01}
):(count(service))

Select Clause (select-clause)¶

The Select clause is used to specify the fields or expressions to be returned by the query, and is one of the most basic and important parts of a DQL query.

Field Selection¶

Basic Syntax¶

// Select a single field
M::cpu:(usage)

// Select multiple fields
M::cpu:(usage, system, user)

// Select all fields
M::cpu:(*)

Field Name Rules¶

Field names can be written in the following forms:

1. Write the name directly: Suitable for ordinary identifiers

   • ✅ message
   • ✅ host_name
   • ✅ response_time

2. Enclosed in backticks: Suitable for field names containing special characters or keywords

   • ✅ message
   • ✅ limit
   • ✅ host-name
   • ✅ column with spaces

3. Avoid these forms: Single and double quotes enclose strings, not field names

   • ❌ 'message'
   • ❌ "message"

JSON Field Extraction¶

When data fields contain JSON-formatted content, a subset of JSON Path syntax can be used to extract data from internal fields.

Basic Syntax¶

field-name@json-path
@json-path                    // Uses the message field by default

JSON Path Syntax¶

• Dot notation for object properties: .field_name
• Bracket notation for object properties: ["key"] (suitable for keys containing spaces or special characters)
• Array index for values: [index]

Application Examples¶

Assuming the following JSON log data:

{
  "message": "User login attempt",
  "request": {
    "method": "POST",
    "path": "/api/login",
    "headers": {
      "user-agent": "Mozilla/5.0",
      "content-type": "application/json"
    },
    "body": {
      "username": "john.doe",
      "password": "***",
      "permissions": ["read", "write", "admin"]
    }
  },
  "response": {
    "status": 200,
    "time": 156,
    "data": [
      {"id": 1, "name": "user1"},
      {"id": 2, "name": "user2"}
    ]
  }
}

// Extract request method
L::auth_logs:([email protected])

// Extract request path
L::auth_logs:([email protected])

// Extract username
L::auth_logs:([email protected])

// Extract response status
L::auth_logs:([email protected])

// Extract User-Agent (contains hyphen, needs brackets)
L::auth_logs:([email protected]["user-agent"])

// Extract the first element of the permissions array
L::auth_logs:([email protected][0])

// Extract the name of the first object in the response data
L::auth_logs:([email protected][0].name)

// Count the number of different request methods
L::auth_logs:(count(*)) [1h] BY [email protected]

// Analyze response time distribution
L::auth_logs:(avg([email protected]), max([email protected])) [1h] BY [email protected]

// Extract multiple fields
L::auth_logs:(
    message,
    [email protected] as method,
    [email protected] as path,
    [email protected] as status,
    [email protected] as response_time
) {[email protected] >= 400} [1h]

Calculated Fields¶

Expression Calculation¶

Supports basic arithmetic operations:

// Unit conversion (milliseconds to seconds)
L::nginx:(response_time / 1000) as response_time_seconds

// Calculate percentage
M::memory:(used / total * 100) as usage_percentage

// Compound calculation
M::network:((bytes_in + bytes_out) / 1024 / 1024) as total_traffic_mb

Function Calculation¶

Supports various aggregation and transformation functions:

// Aggregation functions
M::cpu:(max(usage), min(usage), avg(usage)) [1h] BY host

// Transformation functions
L::logs:(int(response_time) as response_time_seconds)
L::logs:(floor(response_time) as response_time_seconds)

Aliases¶

Assign aliases to fields or expressions to make results more readable and convenient for subsequent reference.

Basic Syntax¶

expression as alias_name

Application Examples¶

// Simple alias
M::cpu:(avg(usage) as avg_usage, max(usage) as max_usage) [1h] BY host

// Expression alias
M::memory:((used / total) * 100 as usage_percent) [1h] BY host

// Function alias
L::logs:(count(*) as error_count) {level = 'error'} [1h] BY service

// JSON extraction alias
L::api_logs:(
    [email protected] as http_method,
    [email protected] as http_status,
    [email protected] as response_time_ms
) [1h]

Usage Tips¶

In DQL, the results of aggregation functions can be directly referenced using the original field name, which reduces the need for aliases:

M::cpu:(max(usage)) [1h] BY host

// The result will contain a max(usage) column, which can be directly referenced later using the usage column name to get the `max(usage)` column from the subquery result:
M::(M::cpu:(max(usage)) [1h] BY host):(max(usage)) { usage > 80 }

However, when multiple aggregation functions use the same field, aliases must be used to correctly distinguish them later:

// Cases where aliases must be used
M::cpu:(max(usage) as max_usage, min(usage) as min_usage) [1h] BY host

Time Clause (time-clause)¶

The time clause is one of the core features of DQL, used to specify the query time interval, aggregation time window, and Rollup aggregation function.

Basic Syntax¶

[start_time:end_time:interval:rollup]

Time Range¶

Absolute Timestamp¶

[1672502400000:1672588800000]     // Millisecond timestamp
[1672502400:1672588800]           // Second timestamp

Relative Time¶

Supports multiple duration units, which can be mixed:

[1h]                             // Past 1 hour to now
[1h:5m]                          // From 1 hour ago to 5 minutes ago
[1h30m]                          // Past 1 hour 30 minutes
[2h15m30s]                       // Past 2 hours 15 minutes 30 seconds

Duration Expression Description¶

When a duration expression is used in a time clause, it represents an offset from the current time backwards. When used in places like the Select clause or Where clause, it is treated as a millisecond integer for calculation.

When used in aggregation queries, two additional duration units are supported:

O::HOST:(count(*)){ `last_update_time` > (now()-10m) } // 10m is treated as the integer 600,000 for calculation
L::*:( count(*) / 1i ) [::1m] // Divide by the time window size (1m) in seconds to calculate the QPS of log writes

Preset Time Ranges¶

Provides commonly used time range keywords:

[TODAY]                          // Data for today
[YESTERDAY]                      // Data for yesterday
[THIS WEEK]                      // Data for this week
[LAST WEEK]                      // Data for last week
[THIS MONTH]                     // Data for this month
[LAST MONTH]                     // Data for last month

When using time range keywords, please confirm that the workspace's timezone setting is correct, and conversion must be strictly based on the user's requested timezone.

Time Window Aggregation¶

Time windows group and aggregate data at specified time intervals. The time column in the returned results represents the start time of each time window.

Single Time Window¶

The entire time range is aggregated into a single value:

M::cpu:(max(usage_total)) [1h]

Query Result:

{
  "columns": ["time", "max(usage_total)"],
  "values": [
    [1721059200000, 37.46]
  ]
}

Time Window Aggregation¶

Group aggregation by time interval:

M::cpu:(max(usage_total)) [1h::10m]

Query Result:

{
  "columns": ["time", "max(usage_total)"],
  "values": [
    [1721059200000, 37.46],
    [1721058600000, 34.12],
    [1721058000000, 33.81],
    [1721057400000, 30.92],
    [1721058000000, 34.53],
    [1721057400000, 36.11]
  ]
}

Rollup Functions¶

Rollup functions are an important preprocessing step in DQL, executed before group aggregation, used to preprocess raw time series data.

Execution Sequence¶

The position of Rollup in the query execution flow:

Raw Data → WHERE Filtering → **Rollup Preprocessing** → Group Aggregation → HAVING Filtering → Final Result

Execution Mechanism¶

The Rollup execution process is divided into two stages:

1. Per Time Series Processing: Apply Rollup functions separately to each independent time series
2. Aggregation Calculation: Perform group aggregation on the results after Rollup processing

Application Scenarios¶

A typical application scenario for Rollup functions is Counter Metrics processing.

For Prometheus Counter type Metrics, aggregating directly using raw values is meaningless because Counters are monotonically increasing. It is necessary to first calculate the rate per time series, and then perform aggregation.

Example Problem: Assume request counters for two servers:*

{
  "host": "web-server-01",
  "data": [
    {"time": "2024-07-15 08:25:00", "request_count": 150},
    {"time": "2024-07-15 08:20:00", "request_count": 140},
    {"time": "2024-07-15 08:15:00", "request_count": 130},
    {"time": "2024-07-15 08:10:00", "request_count": 120},
    {"time": "2024-07-15 08:05:00", "request_count": 110},
    {"time": "2024-07-15 08:00:00", "request_count": 100}
  ]
}

{
  "host": "web-server-02",
  "data": [
    {"time": "2024-07-15 08:25:00", "request_count": 250},
    {"time": "2024-07-15 08:20:00", "request_count": 240},
    {"time": "2024-07-15 08:15:00", "request_count": 230},
    {"time": "2024-07-15 08:10:00", "request_count": 220},
    {"time": "2024-07-15 08:05:00", "request_count": 210},
    {"time": "2024-07-15 08:00:00", "request_count": 200}
  ]
}

Problem with Direct Aggregation:

• The starting value of request_count for web-server-01 is 100
• The starting value of request_count for web-server-02 is 200
• Although both servers have the same request rate (both 10 requests per 5 minutes), the absolute values are different

Solution Using Rollup:

M::http:(sum(request_count)) [rate]

Execution Process:

1. Rollup Phase (executed separately on each time series):

   • web-server-01: rate([100, 110, 120, 130, 140, 150]) = 2 requests/minute
   • web-server-02: rate([200, 210, 220, 230, 240, 250]) = 2 requests/minute

2. Aggregation Phase:

   • sum([2, 2]) = 4 requests/minute

Function Types¶

Common Rollup functions include:

However, almost all aggregation functions that return a single value can be used, so the full list of functions is not listed here.

Default Rollup¶

If no Rollup function is explicitly specified, DQL defaults to no Rollup calculation. PromQL's default Rollup is last, so if you are calculating Prometheus Metrics, be sure to understand this difference and manually specify the Rollup function.

Application Examples¶

// Calculate the total request rate for all servers
M::http_requests:(sum(request_count)) [rate]

// Calculate error rate
M::http_requests:(
    sum(error_count) as errors,
    sum(request_count) as requests
) [rate] BY service

Flexible Time Window Syntax¶

DQL supports various shorthand formats for time windows, making query writing more convenient.

Shorthand Formats¶

[1h]                             // Specify only time range
[1h::5m]                         // Time range + aggregation step
[1h:5m]                          // Start time + end time
[1h:5m:1m]                       // Start + end + step
[1h:5m:1m:avg]                   // Full format
[::5m]                           // Specify only aggregation step
[:::sum]                         // Specify only rollup function
[sum]                            // Specify only rollup function (simplest form)

Filter Conditions (where-clause)¶

Filter conditions are used to filter data rows, retaining only data that meets the conditions for subsequent processing.

Basic Syntax¶

{condition1, condition2, condition3}

Multiple conditions can be connected using commas, AND, OR, &&, ||.

Comparison Operators¶

Pattern Matching Operators¶

Set Operators¶

Logical Operators¶

Tip: The OR operator, besides performing logical OR, also provides a null-value fallback semantics—when the left expression returns NULL, it directly returns the result of the right expression. This can be used to implement priority fallback logic, such as status OR backup_status.

Application Examples¶

Basic Filtering¶

// Single condition
M::cpu:(usage) {host = 'web-01'} [1h]

// Multiple AND conditions
M::cpu:(usage) {host = 'web-01', usage > 80} [1h]

// Using AND keyword
M::cpu:(usage) {host = 'web-01' AND usage > 80} [1h]

// Mixed use of logical operators
M::cpu:(usage) {(host = 'web-01' OR host = 'web-02') AND usage > 80} [1h]

Regular Expression Matching¶

// Match error logs
L::logs:(message) {message =~ 'ERROR.*\\d{4}'} [1h]

// Match logs of specific format
L::logs:(message) {message =~ '\\[(ERROR|WARN)\\].*'} [1h]

// Exclude debug information
L::logs:(message) {message !~ 'DEBUG.*'} [1h]

// Hostname pattern matching
M::cpu:(usage) {host =~ 'web-.*\\.prod\\.com'} [1h]

Set Operations¶

// Status code filtering
L::nginx:(count(*)) {status IN [200, 201, 202, 204]} [1h]

// Exclude specific status codes
L::nginx:(count(*)) {status NOT IN [404, 500, 502]} [1h]

// Log level filtering
L::app_logs:(count(*)) {level IN ['ERROR', 'WARN', 'CRITICAL']} [1h]

Array Field Handling¶

When a field is of array type, DQL supports various array matching operations.

Assuming a field tags = ['web', 'prod', 'api']:

Recommended Syntax: Using IN and NOT IN¶

// Check if array contains a value
{tags IN ['web']}           // true, because tags contains 'web'
{tags IN ['mobile']}        // false, because tags does not contain 'mobile'

// Check if array does not contain a value
{tags NOT IN ['mobile']}    // true, because tags does not contain 'mobile'
{tags NOT IN ['web']}       // false, because tags contains 'web'

// Check if array contains all specified values
{tags IN ['web', 'api']}           // true, contains both 'web' and 'api'
{tags IN ['web', 'api', 'mobile']} // false, does not contain 'mobile'

Compatible Syntax (Not Recommended)¶

The following syntax is retained for historical compatibility and is not recommended for use in new queries. The semantics of these operators on array fields differ from those on ordinary fields and can easily cause confusion.

// Historical syntax: Single-value containment check (overloaded semantics of the equals operator)
{tags = 'web'}           // true, because tags contains 'web'
{tags = 'mobile'}        // false, because tags does not contain 'mobile'

// Historical syntax: Single-value non-containment check (overloaded semantics of the not-equals operator)
{tags != 'mobile'}       // true, because tags does not contain 'mobile'
{tags != 'web'}          // false, because tags contains 'web'

Function Filtering¶

Any function that returns a boolean value can be used as a filter condition.

// String matching
L::logs:(message) { match(message, 'error') }
L::logs:(message) { wildcard(message, 'error*') }

// Query requests with abnormal response time
L::access_logs:(*) {
    response_time > 1000 AND
    match(message, 'timeout')
}

// Query hosts with abnormal memory usage
M::memory:(usage) {
    (usage > 90 OR usage < 10) AND
    host =~ 'prod-.*' AND
    tags IN ['critical', 'important']
}

WHERE Subquery¶

WHERE subqueries are a powerful feature in DQL for implementing dynamic filtering, allowing the results of one query to be used as filter conditions for another query. This mechanism supports dynamic filtering based on data analysis results.

Query Characteristics¶

• Dynamic Filtering: Filter conditions are not fixed values but are dynamically calculated through queries
• Namespace Mixing: Supports cross-namespace queries, enabling correlation analysis across different data types
• Serial Execution: Subqueries execute first, and their results are used for filtering the main query
• Array Results: Subquery results are encapsulated as arrays, therefore only IN and NOT IN operators are supported

Execution Flow¶

Take a typical WHERE subquery as an example:

M::cpu:(avg(usage)) { host IN (O::HOST:(hostname) {provider = 'cloud-a'}) } [1h] BY host

Execution process:

1. Subquery Execution: O::HOST:(hostname) {provider = 'cloud-a'}

   • Query all infrastructure objects
   • Filter out hosts where provider is 'cloud-a'
   • Return hostname list: ['host-01', 'host-02', 'host-03']

2. Main Query Execution: M::cpu:(avg(usage)) [1h] BY host {host IN [...]}

   • Query CPU usage data
   • Only count hosts returned by the subquery
   • Calculate average usage grouped by host

Application Examples¶

// Monitor servers from a specific cloud provider
M::cpu:(avg(usage)) { host IN (O::HOST:(hostname) {provider = 'cloud-a'}) } [1h] BY host


// Compare performance across different cloud providers
M::memory:(avg(used / total * 100)) { host IN (O::HOST:(hostname) {provider IN ['cloud-a', 'cloud-b']}) } [1h] BY host


// Analyze logs for a specific business
L::app_logs:(count(*)) { service IN (T::services:(service_name) {business_unit = 'ecommerce'}) } [1h] BY level


// Monitor application performance for critical business
M::response_time:(avg(response_time)) { service IN (T::services:(service_name) {criticality = 'high'}) } [1h] BY service

Grouping (group-by-clause)¶

Grouping is a core function for data analysis, used to group and aggregate data according to specified dimensions.

Basic Syntax¶

BY expression1, expression2, ...

Grouping Types¶

Field Grouping¶

// Single field grouping
M::cpu:(avg(usage)) [1h] BY host

// Multiple field grouping
M::cpu:(avg(usage)) [1h] BY host, env

// Nested grouping
M::cpu:(avg(usage)) [1h] BY datacenter, rack, host

Expression Grouping¶

// Compound mathematical expression
M::memory:(avg(used)) [1h] BY ((used / total) * 100) as
usage_percent

// Multi-field mathematical operation
M::performance:(avg(response_time)) [1h] BY
(response_time / 1000) as response_seconds

Function Grouping¶

// Drain clustering algorithm
L::logs:(count(*)) BY drain(message, 0.7) as sample

// Regular expression extraction grouping
L::logs:(count(*)) [1h] BY regexp_extract(message, 'error_code: (\\d+)', 1)

Grouping Result Handling¶

When a query includes both grouping and a time window, a two-dimensional data structure is produced. Group By can be used in combination with time windows, and the query result will be a two-dimensional array. The first layer of this two-dimensional array consists of multiple groups distinguished by grouping keys, and the second dimension is the multi-time-interval data within a single group.

Example of a two-dimensional data structure:

Query: M::cpu:(max(usage_total)) [1h::10m] by host

Query Result Structure:

{
  "series": [
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-01"},
      "values": [
        [1721059200000, 78.5],
        [1721058600000, 82.3],
        [1721058000000, 75.8],
        [1721057400000, 88.2]
      ]
    },
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-02"},
      "values": [
        [1721059200000, 45.2],
        [1721058600000, 52.8],
        [1721058000000, 48.5],
        [1721057400000, 61.3]
      ]
    },
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-03"},
      "values": [
        [1721059200000, 92.1],
        [1721058600000, 95.7],
        [1721058000000, 89.4],
        [1721057400000, 97.6]
      ]
    }
  ]
}

Further processing of this two-dimensional array:

1. To filter the results of this two-dimensional array, use the Having clause
2. To sort or paginate data within a single group of the two-dimensional array, use the order by, limit, offset series of statements
3. To sort or paginate the groups of the two-dimensional array, use the sorder by, slimit, soffset series of statements

For a detailed explanation of the two-dimensional data structure and sorting/pagination features, please refer to Sorting and Pagination.

Having Clause (having-clause)¶

The Having clause is used to filter the results after group aggregation, similar to the WHERE clause, but it acts on the aggregated data.

Basic Syntax¶

HAVING condition

Difference from WHERE¶

WHERE and HAVING are both clauses used to filter data, but they operate at different stages of query execution and handle different types of filter conditions.

Difference in Execution Sequence¶

Raw Data → WHERE Filtering → Group Aggregation → HAVING Filtering → Final Result

1. WHERE Clause:

   • Executed before group aggregation
   • Acts on raw data rows
   • Filters out data rows that do not meet the conditions, reducing the amount of data for subsequent processing

2. HAVING Clause:

   • Executed after group aggregation
   • Acts on aggregated results
   • Filters based on the results of aggregation functions

Application Scenarios¶

The HAVING clause is suitable for filtering aggregated results:

// Filtering based on aggregate function values
M::cpu:(avg(usage) as avg_usage) [1h] BY host HAVING avg_usage > 80

// Filtering based on multiple aggregate conditions
M::http_requests:(
    sum(request_count) as total,
    sum(error_count) as errors
) [1h] BY service, endpoint
HAVING errors / total > 0.01 AND total > 1000

// Filtering based on group statistics
L::logs:(count(*) as count) [1h] BY service HAVING count > 100

// Filtering based on compound aggregate conditions
M::response_time:(
    avg(response_time) as avg_time,
    max(response_time) as max_time,
    min(response_time) as min_time
) [1h] BY endpoint
HAVING avg_time > 1000 AND max_time > 5000 AND (max_time - min_time) > 2000

Sorting and Pagination¶

Sorting and pagination in DQL are very important and unique features. They are designed with a dual-sorting mechanism tailored to the characteristics of time series data: intra-group sorting and inter-group sorting. This design enables DQL to efficiently handle complex multi-dimensional time series data analysis requirements.

Understanding DQL's Data Structure¶

Before diving into sorting and pagination, let's understand the two-dimensional data structure of DQL query results. When a query includes both grouping (BY) and a time window, a two-dimensional array is produced:

• First Dimension (Grouping Dimension): Multiple groups distinguished by grouping keys
• Second Dimension (Time Dimension): Data aggregated by time window within each group

For a detailed explanation of the two-dimensional data structure and JSON format examples, please refer to Grouping Result Handling. This two-dimensional structure is the foundation of DQL's sorting and pagination features, and understanding it is crucial for mastering DQL's sorting mechanism.

Intra-group Sorting and Pagination (ORDER BY, LIMIT, OFFSET)¶

Intra-group sorting and pagination act on the second dimension of the two-dimensional structure, i.e., the data within each group. This sorting is executed independently within each group and does not affect data in other groups.

Basic Syntax¶

ORDER BY expression [ASC|DESC]
LIMIT row_count
OFFSET row_offset

Execution Mechanism¶

The execution process of intra-group sorting:

1. Group Processing: Sorting operations are performed independently for each group
2. Sorting Basis: Can use time fields, aggregation function results, or calculation expressions
3. Pagination Limit: LIMIT restricts the number of data rows returned per group
4. Offset Handling: OFFSET skips the first N rows of data in each group

Application Examples¶

Basic Time Sorting¶

// Sort by time descending, show the latest data for each host
M::cpu:(max(usage_total)) [1h::10m] BY host ORDER BY time DESC

// Sort by time ascending, show historical trends
M::cpu:(max(usage_total)) [1h::10m] BY host ORDER BY time ASC

Execution Result (ORDER BY time DESC):

{
  "series": [
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-01"},
      "values": [
        [1721059200000, 78.5],  // 12:00:00
        [1721058600000, 82.3],  // 11:50:00
        [1721058000000, 75.8],  // 11:40:00
        [1721057400000, 88.2],  // 11:30:00
        [1721056800000, 72.1],  // 11:20:00
        [1721056200000, 69.4]   // 11:10:00
      ]
    },
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-02"},
      "values": [
        [1721059200000, 45.2],  // 12:00:00
        [1721058600000, 52.8],  // 11:50:00
        [1721058000000, 48.5],  // 11:40:00
        [1721057400000, 61.3],  // 11:30:00
        [1721056800000, 55.7],  // 11:20:00
        [1721056200000, 58.9]   // 11:10:00
      ]
    }
  ]
}

Sorting Based on Numeric Values¶

// Sort by CPU usage descending, find peak periods for each host
M::cpu:(max(usage_total) as max_usage_total) [1h::10m] BY host ORDER BY max_usage_total DESC

// Sort by response time ascending, find periods with the best performance
M::response_time:(avg(response_time) as avg_response_time) [1h::5m] BY endpoint ORDER BY avg_response_time ASC

Intra-group Pagination¶

// Show only the latest 3 data points for each host
M::cpu:(max(usage_total)) [1h::10m] BY host ORDER BY time DESC LIMIT 3

// Skip the latest 2 data points, show the next 3
M::cpu:(max(usage_total)) [1h::10m] BY host ORDER BY time DESC LIMIT 3 OFFSET 2

Execution Result (LIMIT 3):

{
  "series": [
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-01"},
      "values": [
        [1721059200000, 78.5],  // 12:00:00 - latest
        [1721058600000, 82.3],  // 11:50:00
        [1721058000000, 75.8]   // 11:40:00
        // Only the first 3 data points are returned
      ]
    },
    {
      "columns": ["time", "max(usage_total)"],
      "name": "cpu",
      "tags": {"host": "web-server-02"},
      "values": [
        [1721059200000, 45.2],  // 12:00:00 - latest
        [1721058600000, 52.8],  // 11:50:00
        [1721058000000, 48.5]   // 11:40:00
        // Only the first 3 data points are returned
      ]
    }
  ]
}

Inter-group Sorting and Pagination (SORDER BY, SLIMIT, SOFFSET)¶

Inter-group sorting and pagination are distinctive features of DQL, acting on the first dimension of the two-dimensional structure, i.e., sorting the groups themselves. This sorting requires reducing the data of each group to a single value, then comparing these values across different groups.

When a query does not use BY, the result typically has only one group. In this case, SORDER BY usually has no visible effect on sorting, but SLIMIT / SOFFSET still take effect according to the "number of groups" semantics (i.e., applied to that single group).

Basic Syntax¶

SORDER BY aggregate_function(expression) [ASC|DESC]
SLIMIT group_count
SOFFSET group_offset

Execution Mechanism¶

The execution process of inter-group sorting:

1. Dimensionality Reduction Calculation: Apply an aggregation function to each group to calculate a representative numerical value
2. Group Sorting: Sort all groups based on the dimensionality-reduced value
3. Group Pagination: SLIMIT limits the number of groups returned, SOFFSET skips the first N groups

Selection of Dimensionality Reduction Function¶

Inter-group sorting must use an aggregation function for dimensionality reduction. When no aggregation function is specified, the default aggregation function used is last.

Commonly used dimensionality reduction functions include:

However, almost all aggregation functions that return a single value can be used, so the full list of functions is not detailed here.

Application Examples¶

Sorting Based on Average Value¶

// Sort by average CPU usage descending, find the hosts with the highest load
M::cpu:(avg(usage)) [1h::10m] BY host SORDER BY avg(usage) DESC SLIMIT 5

// Sort by average response time ascending, find the services with the best performance
M::response_time:(avg(response_time)) [1h] BY service SORDER BY avg(response_time) ASC SLIMIT 10

Execution Process Analysis:

1. Dimensionality Reduction Calculation:

   • web-server-01: avg(usage) = 77.7
   • web-server-02: avg(usage) = 53.7
   • web-server-03: avg(usage) = 90.9

2. Group Sorting (by avg(usage) DESC):

   • web-server-03: 90.9
   • web-server-01: 77.7
   • web-server-02: 53.7

3. Final Result (SLIMIT 2):

{
  "series": [
    {
      "columns": ["time", "avg(usage)"],
      "name": "cpu",
      "tags": {"host": "web-server-03"},  // Average usage: 90.9 - Rank 1
      "values": [
        [1721059200000, 92.1],
        [1721058600000, 95.7],
        [1721058000000, 89.4]
      ]
    },
    {
      "columns": ["time", "avg(usage)"],
      "name": "cpu",
      "tags": {"host": "web-server-01"},  // Average usage: 77.7 - Rank 2
      "values": [
        [1721059200000, 78.5],
        [1721058600000, 82.3],
        [1721058000000, 75.8]
      ]
    }
    // web-server-02 (avg: 53.7) is filtered out because SLIMIT 2
  ]
}

Application Examples¶

// Sort by maximum CPU usage, find hosts with abnormal peaks
M::cpu:(max(usage_total)) [1h::10m] BY host SORDER BY max(usage_total) DESC SLIMIT 10

// Sort by minimum memory usage, find hosts with the lowest resource utilization
M::memory:(min(usage_percent)) [24h::1h] BY host SORDER BY min(usage_percent) ASC SLIMIT 5

// Sort by latest CPU usage, find hosts with the highest current load
M::cpu:(usage) [1h::10m] BY host SORDER BY last(usage) DESC SLIMIT 10

// Sort by latest error rate, find services with the most current issues
L::logs:(count(*) as error_count) {level = 'error'} [1h] BY service SORDER BY error_count DESC SLIMIT 5

Combined Use of Dual Sorting and Pagination¶

In practical applications, intra-group sorting and inter-group sorting are often used together to achieve complex data display requirements. This combination can simultaneously control the order of groups and the order of data within groups.

Execution Order¶

The execution order of dual sorting:

1. Inter-group Sorting: First sort and paginate all groups
2. Intra-group Sorting: Perform internal sorting and pagination for the selected groups

Application Examples¶

Monitoring Dashboard Scenario¶

// Find the 10 servers with the highest CPU usage, show the latest 5 data points for each
M::cpu:(avg(usage)) [1h::10m] BY host
SORDER BY avg(usage) DESC SLIMIT 10    // Inter-group sorting: Find the 10 with highest usage
ORDER BY time DESC LIMIT 5             // Intra-group sorting: Show the latest 5 points for each

Execution Result:

{
  "series": [
    {
      "columns": ["time", "avg(usage)"],
      "name": "cpu",
      "tags": {"host": "web-server-03"},  // Average usage: 90.9 - Rank 1
      "values": [
        [1721059200000, 92.1],  // 12:00:00 - latest
        [1721058600000, 95.7],  // 11:50:00
        [1721058000000, 89.4],  // 11:40:00
        [1721057400000, 97.6],  // 11:30:00
        [1721056800000, 87.3]   // 11:20:00
        // Only the latest 5 data points are returned (LIMIT 5)
      ]
    },
    {
      "columns": ["time", "avg(usage)"],
      "name": "cpu",
      "tags": {"host": "web-server-01"},  // Average usage: 77.7 - Rank 2
      "values": [
        [1721059200000, 78.5],  // 12:00:00 - latest
        [1721058600000, 82.3],  // 11:50:00
        [1721058000000, 75.8],  // 11:40:00
        [1721057400000, 88.2],  // 11:30:00
        [1721056800000, 72.1]   // 11:20:00
        // Only the latest 5 data points are returned (LIMIT 5)
      ]
    }
    // Other hosts are filtered out because SLIMIT 10 only returns the 10 with highest usage
  ]
}

By mastering DQL's sorting and pagination features, you can build powerful monitoring dashboards, performance analysis tools, and business insight systems.