How Can I Efficiently Parse Prometheus Data in Go?
Parsing Prometheus Data
Obtaining metrics from Prometheus using an HTTP GET request is just the first step. The next challenge lies in parsing the data and extracting its components. This article explores how to parse Prometheus data, focusing on the EBNF package and alternative solutions.
Parsing with EBNF
The EBNF (Extended Backus-Naur Form) package in Go offers a way to define and parse grammars. While it could be used to parse Prometheus data, it requires significant effort and manual labor. Exponents of this method must meticulously create the grammar, anticipating all possible data variations to ensure accurate parsing.
Leveraging Prometheus's expfmt
Instead of relying on complex grammar definitions, you can take advantage of a package developed by the Prometheus authors themselves - expfmt. This Go library specializes in encoding and decoding the Prometheus Exposition Format (EBNF-based). Its ease of use and out-of-the-box functionality make it an ideal choice for parsing Prometheus data.
An Example with expfmt
Consider the following sample Prometheus data:
# HELP net_conntrack_dialer_conn_attempted_total # TYPE net_conntrack_dialer_conn_attempted_total untyped net_conntrack_dialer_conn_attempted_total{dialer_name="federate",instance="localhost:9090",job="prometheus"} 1 1608520832877
The following Go code demonstrates how to parse this data using expfmt:
package main import ( "flag" "fmt" "log" "os" dto "github.com/prometheus/client_model/go" "github.com/prometheus/common/expfmt" ) func main() { f := flag.String("f", "", "set filepath") flag.Parse() mf, err := parseMF(*f) fatal(err) for k, v := range mf { fmt.Println("KEY: ", k) fmt.Println("VAL: ", v) } } func parseMF(path string) (map[string]*dto.MetricFamily, error) { reader, err := os.Open(path) if err != nil { return nil, err } var parser expfmt.TextParser mf, err := parser.TextToMetricFamilies(reader) if err != nil { return nil, err } return mf, nil } func fatal(err error) { if err != nil { log.Fatalln(err) } }
Running this program produces the following output:
KEY: net_conntrack_dialer_conn_attempted_total VAL: name:"net_conntrack_dialer_conn_attempted_total" type:UNTYPED metric:<label:<name:"dialer_name" value:"federate" > label:<name:"instance" value:"localhost:9090" > label:<name:"job" value:"prometheus" > untyped:<value:1 > timestamp_ms:1608520832877 >
Troubleshooting Formatting Issues
Ensure that the Prometheus data adheres to the proper formatting requirements. Each line must conclude with a line-feed character 'n'. Deviations from this format, including 'r' or 'rn', will trigger protocol errors.
The above is the detailed content of How Can I Efficiently Parse Prometheus Data in Go?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Go language performs well in building efficient and scalable systems. Its advantages include: 1. High performance: compiled into machine code, fast running speed; 2. Concurrent programming: simplify multitasking through goroutines and channels; 3. Simplicity: concise syntax, reducing learning and maintenance costs; 4. Cross-platform: supports cross-platform compilation, easy deployment.

Golang is better than Python in terms of performance and scalability. 1) Golang's compilation-type characteristics and efficient concurrency model make it perform well in high concurrency scenarios. 2) Python, as an interpreted language, executes slowly, but can optimize performance through tools such as Cython.

Golang is better than C in concurrency, while C is better than Golang in raw speed. 1) Golang achieves efficient concurrency through goroutine and channel, which is suitable for handling a large number of concurrent tasks. 2)C Through compiler optimization and standard library, it provides high performance close to hardware, suitable for applications that require extreme optimization.

Goimpactsdevelopmentpositivelythroughspeed,efficiency,andsimplicity.1)Speed:Gocompilesquicklyandrunsefficiently,idealforlargeprojects.2)Efficiency:Itscomprehensivestandardlibraryreducesexternaldependencies,enhancingdevelopmentefficiency.3)Simplicity:

Golang and Python each have their own advantages: Golang is suitable for high performance and concurrent programming, while Python is suitable for data science and web development. Golang is known for its concurrency model and efficient performance, while Python is known for its concise syntax and rich library ecosystem.

The performance differences between Golang and C are mainly reflected in memory management, compilation optimization and runtime efficiency. 1) Golang's garbage collection mechanism is convenient but may affect performance, 2) C's manual memory management and compiler optimization are more efficient in recursive computing.

Golang is suitable for rapid development and concurrent scenarios, and C is suitable for scenarios where extreme performance and low-level control are required. 1) Golang improves performance through garbage collection and concurrency mechanisms, and is suitable for high-concurrency Web service development. 2) C achieves the ultimate performance through manual memory management and compiler optimization, and is suitable for embedded system development.

Golang and C each have their own advantages in performance competitions: 1) Golang is suitable for high concurrency and rapid development, and 2) C provides higher performance and fine-grained control. The selection should be based on project requirements and team technology stack.
