add feature esql search (#90)

This commit is contained in:
T. von Dein
2026-07-10 15:07:57 +02:00
parent 15dbf7ada0
commit f61fda0697
7 changed files with 1118 additions and 5 deletions

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@@ -576,6 +576,7 @@ version - show esctl version information
debug - developer only
help-jsonpath - show jsonpath help
help-usage - show overview of all available commands
help-esql - show esql help
```
# Development

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@@ -24,10 +24,11 @@ import (
"github.com/go-openapi/swag/loading"
)
//go:embed *.json
//go:embed *.json *.md
var AssetFS embed.FS
var OpenAPI *loads.Document
var EsQlCheatSheet string
func LoadAssetOpenApi() {
doc, err := loads.Spec(
@@ -41,3 +42,12 @@ func LoadAssetOpenApi() {
OpenAPI = doc
}
func LoadEsql() {
md, err := AssetFS.ReadFile("esql.md")
if err != nil {
panic(err)
}
EsQlCheatSheet = string(md)
}

951
assets/esql.md Normal file
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@@ -0,0 +1,951 @@
<!-- courtesy https://github.com/linkan-per/ES-QL-Cheat-Sheet by |linkan-per -->
# ES|QL (Elasticsearch Query Language) Cheat Sheet
## Table of Contents
1. [Introduction](#introduction)
2. [Basic Query Structure](#basic-query-structure)
3. [Source Commands](#source-commands)
4. [Processing Commands](#processing-commands)
5. [Data Selection & Filtering](#data-selection--filtering)
6. [Aggregations & Statistics](#aggregations--statistics)
7. [String Functions](#string-functions)
8. [Mathematical Functions](#mathematical-functions)
9. [Date/Time Functions](#datetime-functions)
10. [Type Conversion Functions](#type-conversion-functions)
11. [Conditional Functions](#conditional-functions)
12. [Array Functions](#array-functions)
13. [Join Operations (LOOKUP)](#join-operations-lookup)
14. [Sorting & Limiting](#sorting--limiting)
15. [Grouping & Aggregating](#grouping--aggregating)
16. [Advanced Patterns](#advanced-patterns)
---
## Introduction
ES|QL is Elasticsearch's new query language designed for data exploration, analysis, and transformation. It uses a pipe (`|`) syntax to chain commands together.
**Basic Syntax:**
```
FROM <data-source>
| <processing-command>
| <processing-command>
| ...
```
---
## Basic Query Structure
### Simple Query
```esql
FROM logs-*
| LIMIT 10
```
### Query with Multiple Commands
```esql
FROM employees
| WHERE department == "Engineering"
| KEEP name, salary, hire_date
| SORT salary DESC
| LIMIT 5
```
---
## Source Commands
### FROM - Specify Data Source
```esql
// From a single index
FROM logs-2024
// From multiple indices with wildcard
FROM logs-*, metrics-*
// From specific indices
FROM index1, index2, index3
// With metadata
FROM logs-* METADATA _id, _index
```
### ROW - Generate Inline Data
```esql
// Create a single row
ROW name = "John", age = 30, city = "NYC"
// Multiple rows
ROW a = 1, b = "x"
| EVAL c = a * 10
```
---
## Processing Commands
### KEEP - Select Specific Fields
```esql
FROM employees
| KEEP name, department, salary
// Keep with pattern
FROM logs-*
| KEEP @timestamp, message, host.*
```
### DROP - Remove Specific Fields
```esql
FROM employees
| DROP password, ssn, internal_notes
// Drop with pattern
FROM logs-*
| DROP *.keyword
```
### RENAME - Rename Fields
```esql
FROM employees
| RENAME emp_name AS name, emp_dept AS department
// Multiple renames
FROM logs-*
| RENAME source.ip AS src_ip, destination.ip AS dst_ip
```
---
## Data Selection & Filtering
### WHERE - Filter Rows
```esql
// Equality
FROM employees
| WHERE department == "Sales"
// Comparison operators
FROM products
| WHERE price > 100 AND stock < 50
// IS NULL / IS NOT NULL
FROM logs-*
| WHERE error_code IS NOT NULL
// IN operator
FROM employees
| WHERE department IN ("Sales", "Marketing", "HR")
// LIKE operator (wildcards)
FROM logs-*
| WHERE message LIKE "*error*"
// RLIKE operator (regex)
FROM logs-*
| WHERE message RLIKE "error|exception|failure"
// NOT operator
FROM employees
| WHERE NOT department == "IT"
// Multiple conditions
FROM orders
| WHERE status == "completed"
AND total_amount > 1000
AND order_date >= "2024-01-01"
```
---
## Aggregations & Statistics
### STATS - Aggregate Functions
#### COUNT
```esql
// Count all rows
FROM logs-*
| STATS count = COUNT()
// Count distinct
FROM employees
| STATS unique_departments = COUNT_DISTINCT(department)
// Count by group
FROM logs-*
| STATS event_count = COUNT() BY log_level
```
#### SUM, AVG, MIN, MAX
```esql
FROM sales
| STATS
total_revenue = SUM(amount),
avg_sale = AVG(amount),
min_sale = MIN(amount),
max_sale = MAX(amount)
// With grouping
FROM sales
| STATS
total = SUM(amount),
average = AVG(amount)
BY product_category
```
#### MEDIAN, PERCENTILE
```esql
FROM response_times
| STATS
median_time = MEDIAN(duration),
p95 = PERCENTILE(duration, 95),
p99 = PERCENTILE(duration, 99)
```
#### Multiple Aggregations
```esql
FROM orders
| STATS
order_count = COUNT(),
total_revenue = SUM(amount),
avg_order_value = AVG(amount),
unique_customers = COUNT_DISTINCT(customer_id)
BY region, product_category
```
---
## String Functions
### CONCAT - Concatenate Strings
```esql
FROM employees
| EVAL full_name = CONCAT(first_name, " ", last_name)
// With separator
FROM logs-*
| EVAL log_info = CONCAT(level, ": ", message)
```
### SUBSTRING - Extract Substring
```esql
FROM employees
| EVAL first_initial = SUBSTRING(first_name, 0, 1)
// Extract with length
FROM products
| EVAL short_code = SUBSTRING(product_id, 0, 5)
```
### LENGTH - String Length
```esql
FROM messages
| EVAL message_length = LENGTH(message)
| WHERE message_length > 100
```
### TRIM, LTRIM, RTRIM - Remove Whitespace
```esql
FROM user_input
| EVAL cleaned = TRIM(input_field)
| EVAL left_trimmed = LTRIM(input_field)
| EVAL right_trimmed = RTRIM(input_field)
```
### UPPER, LOWER - Case Conversion
```esql
FROM employees
| EVAL name_upper = UPPER(name)
| EVAL email_lower = LOWER(email)
```
### REPLACE - Replace String
```esql
FROM logs-*
| EVAL cleaned_message = REPLACE(message, "ERROR", "Warning")
```
### SPLIT - Split String into Array
```esql
FROM logs-*
| EVAL tags_array = SPLIT(tags, ",")
```
### STARTS_WITH, ENDS_WITH
```esql
FROM files
| WHERE STARTS_WITH(filename, "log_")
| WHERE ENDS_WITH(filename, ".txt")
```
---
## Mathematical Functions
### Basic Operations
```esql
FROM sales
| EVAL
total = price * quantity,
discount_price = price * 0.9,
tax = price * 0.08
// Multiple operations
FROM metrics
| EVAL
sum_val = field1 + field2,
diff_val = field1 - field2,
product = field1 * field2,
ratio = field1 / field2,
remainder = field1 % field2
```
### ABS - Absolute Value
```esql
FROM transactions
| EVAL abs_amount = ABS(transaction_amount)
```
### ROUND, FLOOR, CEIL
```esql
FROM measurements
| EVAL
rounded = ROUND(value, 2),
floored = FLOOR(value),
ceiled = CEIL(value)
```
### POW - Power
```esql
FROM data
| EVAL squared = POW(value, 2)
| EVAL cubed = POW(value, 3)
```
### SQRT - Square Root
```esql
FROM measurements
| EVAL sqrt_value = SQRT(value)
```
### LOG, LOG10
```esql
FROM data
| EVAL
natural_log = LOG(value),
log_base_10 = LOG10(value)
```
### GREATEST, LEAST
```esql
FROM comparisons
| EVAL
max_val = GREATEST(val1, val2, val3),
min_val = LEAST(val1, val2, val3)
```
---
## Date/Time Functions
### NOW - Current Timestamp
```esql
FROM logs-*
| EVAL current_time = NOW()
```
### DATE_EXTRACT - Extract Date Parts
```esql
FROM events
| EVAL
year = DATE_EXTRACT("year", @timestamp),
month = DATE_EXTRACT("month", @timestamp),
day = DATE_EXTRACT("day", @timestamp),
hour = DATE_EXTRACT("hour", @timestamp),
minute = DATE_EXTRACT("minute", @timestamp),
day_of_week = DATE_EXTRACT("day_of_week", @timestamp)
```
### DATE_FORMAT - Format Date
```esql
FROM events
| EVAL formatted_date = DATE_FORMAT("yyyy-MM-dd", @timestamp)
| EVAL custom_format = DATE_FORMAT("MMM dd, yyyy HH:mm", @timestamp)
```
### DATE_TRUNC - Truncate Date
```esql
FROM logs-*
| EVAL
hour_bucket = DATE_TRUNC("hour", @timestamp),
day_bucket = DATE_TRUNC("day", @timestamp),
month_bucket = DATE_TRUNC("month", @timestamp)
```
### DATE_DIFF - Date Difference
```esql
FROM orders
| EVAL days_since_order = DATE_DIFF("days", order_date, NOW())
| EVAL hours_to_delivery = DATE_DIFF("hours", order_date, delivery_date)
```
### DATE_PARSE - Parse String to Date
```esql
FROM data
| EVAL parsed_date = DATE_PARSE("yyyy-MM-dd", date_string)
```
---
## Type Conversion Functions
### TO_STRING - Convert to String
```esql
FROM data
| EVAL id_string = TO_STRING(id)
| EVAL amount_string = TO_STRING(amount)
```
### TO_INTEGER, TO_LONG - Convert to Integer
```esql
FROM data
| EVAL age_int = TO_INTEGER(age_string)
| EVAL id_long = TO_LONG(id_string)
```
### TO_DOUBLE - Convert to Double
```esql
FROM data
| EVAL price_double = TO_DOUBLE(price_string)
```
### TO_BOOLEAN - Convert to Boolean
```esql
FROM data
| EVAL is_active = TO_BOOLEAN(active_string)
```
### TO_DATETIME - Convert to DateTime
```esql
FROM data
| EVAL timestamp = TO_DATETIME(date_string)
```
### TO_IP - Convert to IP Address
```esql
FROM logs-*
| EVAL ip_address = TO_IP(ip_string)
```
---
## Conditional Functions
### CASE - Conditional Logic
```esql
FROM employees
| EVAL salary_grade = CASE(
salary < 50000, "Entry",
salary < 80000, "Mid",
salary < 120000, "Senior",
"Executive"
)
// With multiple conditions
FROM orders
| EVAL order_status = CASE(
status == "pending" AND days_old > 7, "Overdue",
status == "pending", "Processing",
status == "shipped", "In Transit",
status == "delivered", "Completed",
"Unknown"
)
```
### COALESCE - Return First Non-Null Value
```esql
FROM data
| EVAL display_name = COALESCE(nickname, first_name, username, "Unknown")
```
### IF - Simple Conditional
```esql
FROM products
| EVAL stock_status =
CASE(stock > 0, "Available", "Out of Stock")
// Nested conditions
FROM employees
| EVAL bonus = CASE(
performance_rating >= 4.5, salary * 0.15,
performance_rating >= 3.5, salary * 0.10,
performance_rating >= 2.5, salary * 0.05,
0
)
```
---
## Array Functions
### MV_COUNT - Count Array Elements
```esql
FROM logs-*
| EVAL tag_count = MV_COUNT(tags)
| WHERE tag_count > 3
```
### MV_AVG, MV_SUM, MV_MIN, MV_MAX - Array Aggregations
```esql
FROM metrics
| EVAL
avg_value = MV_AVG(values),
total = MV_SUM(values),
min_value = MV_MIN(values),
max_value = MV_MAX(values)
```
### MV_CONCAT - Concatenate Array Elements
```esql
FROM logs-*
| EVAL all_tags = MV_CONCAT(tags, ", ")
```
### MV_DEDUPE - Remove Duplicates from Array
```esql
FROM data
| EVAL unique_values = MV_DEDUPE(values)
```
### MV_FIRST, MV_LAST - Get First/Last Element
```esql
FROM logs-*
| EVAL first_tag = MV_FIRST(tags)
| EVAL last_tag = MV_LAST(tags)
```
### MV_SLICE - Extract Array Slice
```esql
FROM data
| EVAL first_three = MV_SLICE(values, 0, 3)
```
---
## Join Operations (LOOKUP)
ES|QL uses ENRICH (similar to LOOKUP/JOIN) to join data from enrich policies.
### Prerequisites: Create Enrich Policy
First, create an enrich policy in Kibana Dev Tools:
```json
PUT /_enrich/policy/user_lookup
{
"match": {
"indices": "users",
"match_field": "user_id",
"enrich_fields": ["username", "email", "department"]
}
}
POST /_enrich/policy/user_lookup/_execute
```
### ENRICH - Join/Lookup Data
```esql
FROM logs-*
| ENRICH user_lookup ON user_id
| KEEP @timestamp, user_id, username, email, message
// With field renaming
FROM transactions
| ENRICH product_lookup ON product_id WITH product_name, category, price
| KEEP transaction_id, product_name, category, quantity, price
// Multiple enrichments
FROM orders
| ENRICH customer_lookup ON customer_id WITH customer_name, customer_tier
| ENRICH product_lookup ON product_id WITH product_name, product_category
| KEEP order_id, customer_name, product_name, order_amount
```
### Complex Join Example
```esql
FROM orders
| ENRICH customer_lookup ON customer_id
WITH customer_name, customer_email, customer_segment
| ENRICH product_lookup ON product_id
WITH product_name, product_category, product_price
| EVAL total_price = quantity * product_price
| WHERE customer_segment == "Premium"
| STATS
total_orders = COUNT(),
total_revenue = SUM(total_price)
BY customer_name, product_category
| SORT total_revenue DESC
```
---
## Sorting & Limiting
### SORT - Order Results
```esql
// Ascending order (default)
FROM employees
| SORT salary
// Descending order
FROM employees
| SORT salary DESC
// Multiple fields
FROM employees
| SORT department ASC, salary DESC
// With nulls first/last
FROM data
| SORT value DESC NULLS FIRST
```
### LIMIT - Limit Results
```esql
// Get first 10 rows
FROM logs-*
| LIMIT 10
// Top 5 highest salaries
FROM employees
| SORT salary DESC
| LIMIT 5
// Pagination (skip and limit)
FROM products
| SORT price
| LIMIT 20 // Results 0-19
```
### HEAD - Get First N Rows (Alias for LIMIT)
```esql
FROM logs-*
| HEAD 100
```
---
## Grouping & Aggregating
### GROUP BY with STATS
```esql
// Single field grouping
FROM sales
| STATS total_sales = SUM(amount) BY region
// Multiple field grouping
FROM orders
| STATS
order_count = COUNT(),
total_revenue = SUM(amount)
BY region, product_category, sales_rep
// Time-based grouping
FROM logs-*
| EVAL hour = DATE_TRUNC("hour", @timestamp)
| STATS event_count = COUNT() BY hour, log_level
| SORT hour DESC
```
### Complex Aggregation Example
```esql
FROM sales_data
| WHERE order_date >= "2024-01-01"
| EVAL month = DATE_TRUNC("month", order_date)
| STATS
total_orders = COUNT(),
total_revenue = SUM(amount),
avg_order_value = AVG(amount),
unique_customers = COUNT_DISTINCT(customer_id),
max_order = MAX(amount),
min_order = MIN(amount)
BY month, region, product_category
| EVAL revenue_per_customer = total_revenue / unique_customers
| WHERE total_orders > 100
| SORT month DESC, total_revenue DESC
| LIMIT 50
```
---
## Advanced Patterns
### Window Functions Pattern
```esql
// Running total by group
FROM sales
| SORT date
| STATS
daily_sales = SUM(amount),
order_count = COUNT()
BY date, region
| SORT region, date
```
### Pivoting Data
```esql
// Count by status and priority
FROM tickets
| STATS ticket_count = COUNT() BY status, priority
| SORT status, priority
```
### Finding Duplicates
```esql
FROM users
| STATS count = COUNT() BY email
| WHERE count > 1
| SORT count DESC
```
### Time Series Analysis
```esql
FROM metrics-*
| EVAL
hour = DATE_TRUNC("hour", @timestamp),
day = DATE_EXTRACT("day", @timestamp)
| STATS
avg_cpu = AVG(cpu_percent),
max_cpu = MAX(cpu_percent),
avg_memory = AVG(memory_percent)
BY hour, host
| WHERE avg_cpu > 80
| SORT hour DESC
```
### Percentage Calculations
```esql
FROM sales
| STATS
total_sales = SUM(amount),
count = COUNT()
BY product_category
| EVAL percentage = ROUND(total_sales / SUM(total_sales) * 100, 2)
| SORT percentage DESC
```
### Top N per Group
```esql
// Top 3 products per category by sales
FROM sales
| STATS total_sales = SUM(amount) BY product_category, product_name
| SORT product_category, total_sales DESC
// Note: ES|QL doesn't have native PARTITION BY,
// so you may need to process this in multiple queries or use aggregations
```
### Data Cleaning
```esql
FROM raw_data
| EVAL
// Clean whitespace
cleaned_name = TRIM(name),
// Standardize case
email_lower = LOWER(email),
// Replace values
status = REPLACE(status, "N/A", "Unknown"),
// Handle nulls
age = COALESCE(age, 0),
// Validate ranges
valid_age = CASE(age < 0 OR age > 150, NULL, age)
| WHERE cleaned_name IS NOT NULL
| DROP name, email
| RENAME cleaned_name AS name, email_lower AS email
```
### Cohort Analysis
```esql
FROM user_events
| EVAL
signup_month = DATE_TRUNC("month", signup_date),
event_month = DATE_TRUNC("month", event_date)
| STATS
active_users = COUNT_DISTINCT(user_id)
BY signup_month, event_month
| SORT signup_month, event_month
```
### Anomaly Detection Pattern
```esql
FROM metrics-*
| EVAL hour = DATE_TRUNC("hour", @timestamp)
| STATS
avg_value = AVG(value),
stddev = SQRT(AVG(POW(value - AVG(value), 2)))
BY hour
| EVAL
upper_bound = avg_value + (2 * stddev),
lower_bound = avg_value - (2 * stddev)
```
---
## Complete Real-World Examples
### Example 1: User Activity Dashboard
```esql
FROM user_logs-*
| WHERE @timestamp >= NOW() - 7 days
| ENRICH user_lookup ON user_id WITH username, user_tier
| EVAL day = DATE_TRUNC("day", @timestamp)
| STATS
daily_active_users = COUNT_DISTINCT(user_id),
total_sessions = COUNT(),
avg_session_duration = AVG(session_duration)
BY day, user_tier
| EVAL avg_duration_minutes = ROUND(avg_session_duration / 60, 2)
| SORT day DESC, user_tier
```
### Example 2: E-commerce Sales Report
```esql
FROM orders
| WHERE order_date >= "2024-01-01"
| ENRICH customer_lookup ON customer_id
WITH customer_name, customer_segment
| ENRICH product_lookup ON product_id
WITH product_name, product_category, cost_price
| EVAL
profit = (price - cost_price) * quantity,
month = DATE_TRUNC("month", order_date)
| STATS
total_orders = COUNT(),
total_revenue = SUM(price * quantity),
total_profit = SUM(profit),
avg_order_value = AVG(price * quantity),
unique_customers = COUNT_DISTINCT(customer_id)
BY month, product_category, customer_segment
| EVAL profit_margin = ROUND(total_profit / total_revenue * 100, 2)
| WHERE total_revenue > 10000
| SORT month DESC, total_revenue DESC
| LIMIT 100
```
### Example 3: Security Log Analysis
```esql
FROM security-logs-*
| WHERE @timestamp >= NOW() - 24 hours
| WHERE event_type IN ("login_failed", "suspicious_activity")
| EVAL hour = DATE_TRUNC("hour", @timestamp)
| STATS
event_count = COUNT(),
unique_ips = COUNT_DISTINCT(source_ip),
unique_users = COUNT_DISTINCT(username)
BY hour, event_type, country
| WHERE event_count > 100
| SORT hour DESC, event_count DESC
```
### Example 4: Application Performance Monitoring
```esql
FROM apm-*
| WHERE @timestamp >= NOW() - 1 hour
| EVAL
response_category = CASE(
response_time < 100, "Fast",
response_time < 500, "Medium",
response_time < 1000, "Slow",
"Very Slow"
),
minute = DATE_TRUNC("minute", @timestamp)
| STATS
request_count = COUNT(),
avg_response = AVG(response_time),
p95_response = PERCENTILE(response_time, 95),
p99_response = PERCENTILE(response_time, 99),
error_count = COUNT() WHERE status_code >= 400
BY minute, endpoint, response_category
| EVAL error_rate = ROUND(error_count / request_count * 100, 2)
| WHERE request_count > 10
| SORT minute DESC, avg_response DESC
```
---
## Tips & Best Practices
1. **Use KEEP instead of SELECT** - More explicit about which fields to retain
2. **Filter early with WHERE** - Reduce data processing by filtering before aggregations
3. **Use DATE_TRUNC for time bucketing** - Essential for time series analysis
4. **Leverage ENRICH for joins** - Pre-create enrich policies for frequently joined data
5. **Use EVAL for calculated fields** - Create derived fields before aggregation
6. **Combine multiple conditions in WHERE** - More efficient than multiple WHERE clauses
7. **Use STATS with BY for grouping** - Replaces traditional GROUP BY
8. **Sort after aggregation** - More efficient than sorting before
9. **Use LIMIT to control output size** - Especially important for large datasets
10. **Use metadata fields when needed** - Access _id, _index with METADATA keyword
---
## Common Patterns Cheat Sheet
```esql
// Count by field
FROM index | STATS count = COUNT() BY field
// Top N
FROM index | STATS value = SUM(amount) BY category | SORT value DESC | LIMIT 10
// Time series
FROM index | EVAL bucket = DATE_TRUNC("hour", @timestamp) | STATS count = COUNT() BY bucket
// Percentage of total
FROM index | STATS total = SUM(amount) BY category | EVAL pct = total / SUM(total) * 100
// Filter nulls
FROM index | WHERE field IS NOT NULL
// String matching
FROM index | WHERE field LIKE "*pattern*"
// Date range
FROM index | WHERE @timestamp >= NOW() - 7 days
// Multiple aggregations
FROM index | STATS count = COUNT(), sum = SUM(val), avg = AVG(val) BY group
// Conditional aggregation
FROM index | STATS error_count = COUNT() WHERE status == "error" BY service
```
---
## Comparison with Traditional SQL
| SQL | ES|QL |
|-----|-------|
| SELECT * | FROM index |
| SELECT field1, field2 | FROM index \| KEEP field1, field2 |
| WHERE condition | WHERE condition (same) |
| GROUP BY field | STATS ... BY field |
| ORDER BY field | SORT field |
| LIMIT 10 | LIMIT 10 (same) |
| COUNT(*) | STATS count = COUNT() |
| SUM(field) | STATS total = SUM(field) |
| AVG(field) | STATS avg = AVG(field) |
| JOIN | ENRICH (using enrich policies) |
| CASE WHEN | CASE(...) |
| CONCAT(a, b) | CONCAT(a, b) (same) |
---
## Resources
- Official ES|QL Documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/esql.html
- ES|QL Functions Reference: https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions.html
- Enrich Processor: https://www.elastic.co/guide/en/elasticsearch/reference/current/enrich-processor.html
---
*Last Updated: 2024*
*ES|QL is actively evolving - check official documentation for latest features*

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@@ -24,10 +24,13 @@ import (
"runtime/pprof"
"strings"
"codeberg.org/scip/esctl/assets"
"codeberg.org/scip/esctl/pkg/cfg"
"codeberg.org/scip/esctl/pkg/es"
"codeberg.org/scip/esctl/pkg/log"
"codeberg.org/scip/esctl/pkg/printer"
markdown "github.com/MichaelMure/go-term-markdown"
"github.com/urfave/cli/v3"
)
@@ -141,7 +144,7 @@ func Main() int {
Name: "output",
Aliases: []string{"o"},
Value: "",
Usage: "output mode (tsv, json, yaml) default: tsv",
Usage: "output mode (tsv, csv, json, yaml) default: tsv",
Destination: &conf.Output,
},
&cli.StringFlag{
@@ -164,6 +167,7 @@ func Main() int {
Node(conf),
Roles(conf),
Search(conf),
SearchQL(conf),
Shard(conf),
Snapshot(conf),
Task(conf),
@@ -171,6 +175,7 @@ func Main() int {
Debug(conf),
HelpJsonPath(conf),
HelpUsage(conf),
HelpEsQl(conf),
},
Before: func(ctx context.Context, cmd *cli.Command) (context.Context, error) {
@@ -226,6 +231,22 @@ func HelpJsonPath(conf *cfg.Config) *cli.Command {
}
}
func HelpEsQl(conf *cfg.Config) *cli.Command {
return &cli.Command{
Name: "help-esql",
Usage: "show esql help",
Action: func(ctx context.Context, cmd *cli.Command) error {
assets.LoadEsql()
width := cfg.GetTermWidth()
printer.Pager("esql cheat sheet", string(markdown.Render(assets.EsQlCheatSheet, width, cfg.DefaultMargin)))
return nil
},
}
}
func Version(conf *cfg.Config) *cli.Command {
return &cli.Command{
Name: "version",

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@@ -18,6 +18,7 @@ package cmd
import (
"context"
"strings"
"codeberg.org/scip/esctl/pkg/cfg"
"codeberg.org/scip/esctl/pkg/es"
@@ -45,6 +46,11 @@ https://www.elastic.co/docs/reference/elasticsearch/rest-apis/common-options#dat
For timestamp formats refer to:
https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/mapping-date-format`
const QlUsage = `ES|QL documentation:
https://www.elastic.co/docs/reference/query-languages/esql/esql-getting-started
See also: esctl help-esql`
func Search(conf *cfg.Config) *cli.Command {
return &cli.Command{
Name: "search",
@@ -149,3 +155,19 @@ func Search(conf *cfg.Config) *cli.Command {
},
}
}
func SearchQL(conf *cfg.Config) *cli.Command {
return &cli.Command{
Name: "searchql",
Aliases: []string{"/"},
Usage: "search using ES/QL language",
UsageText: "search <ES/QL term>",
CustomHelpTemplate: addReference(QlUsage),
Action: func(ctx context.Context, cmd *cli.Command) error {
args := cmd.Args()
return es.SearchQL(conf, strings.Join(args.Slice(), " "))
},
}
}

79
pkg/es/searchql.go Normal file
View File

@@ -0,0 +1,79 @@
/*
Copyright © 2026 Thomas von Dein
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package es
import (
"context"
"encoding/json"
"fmt"
"log/slog"
"codeberg.org/scip/esctl/pkg/cfg"
"codeberg.org/scip/esctl/pkg/printer"
)
type searchQlColumn struct {
Name string `json:"name"`
Type string `json:"type"`
}
type searchQlResult struct {
Partial bool `json:"is_partial"`
Docs int `json:"documents_found"`
Columns []searchQlColumn `json:"columns"`
Values [][]any `json:"values"`
}
func SearchQL(conf *cfg.Config, querystring string) error {
query := conf.DefaultCluster.ES().Esql.Query().
Query(querystring).
DropNullColumns(true)
res, err := query.Do(context.Background())
if err != nil {
return fmt.Errorf("failed to run esql query: %w", esErrorString(err))
}
if conf.Debug {
fmt.Println(string(res))
}
qlResult := searchQlResult{}
if err = json.Unmarshal(res, &qlResult); err != nil {
return fmt.Errorf("failed to unmarshal esql result: %w", err)
}
slog.Debug("searchql result", "qlres", qlResult)
if qlResult.Docs == 0 {
return nil
}
table := printer.NewTable(conf, len(qlResult.Columns), len(qlResult.Values))
headers := make([]string, len(qlResult.Columns))
for idx, col := range qlResult.Columns {
headers[idx] = col.Name
}
table.Addheaders(headers...)
table.Entries = qlResult.Values
return table.Print()
}

View File

@@ -30,9 +30,10 @@ import (
)
type Table struct {
Mode string // tsv, json, yaml
Headers []string
Entries [][]any
Mode string // tsv, json, yaml
Headers []string
RawHeaders []string
Entries [][]any
rows [][]string // representation used for printing
processed bool
@@ -45,6 +46,7 @@ func NewTable(conf *cfg.Config, columns, rows int) *Table {
table := Table{Mode: conf.Output, maxwidth: cfg.GetTermWidth()}
table.Headers = make([]string, columns)
table.RawHeaders = make([]string, columns)
table.Entries = make([][]any, rows)
table.lenHeaders = make([]int, columns)
table.alignInts = conf.AlignInts
@@ -65,6 +67,7 @@ func (table *Table) WithHeaders(headers ...string) *Table {
table.Entries = [][]any{}
table.lenHeaders = make([]int, count)
table.Headers = make([]string, count)
table.RawHeaders = make([]string, count)
table.Addheaders(headers...)
@@ -77,6 +80,8 @@ func (table *Table) Print() error {
return table.PrintJSON()
case "yaml":
return table.PrintYAML()
case "csv":
return table.PrintCSV()
default:
return table.PrintTSV()
}
@@ -178,6 +183,28 @@ func (table *Table) PrintTSV() error {
return nil
}
func (table *Table) PrintCSV() error {
table.preprocessRows()
fmt.Println(strings.Join(table.RawHeaders, ","))
for _, entries := range table.rows {
row := make([]string, len(entries))
for idx, entry := range entries {
if strings.Contains(entry, " ") || strings.Contains(entry, ",") {
row[idx] = `"` + entry + `"`
} else {
row[idx] = entry
}
}
fmt.Println(strings.Join(row, ","))
}
return nil
}
func (table *Table) Sort() {
// sanity checks
if len(table.Entries) == 0 {
@@ -199,6 +226,8 @@ func (table *Table) Addheaders(headers ...string) {
default:
table.Headers[idx] = bold(strings.ReplaceAll(strings.ToUpper(header), " ", "-"))
}
table.RawHeaders[idx] = header
}
}