Narciso  Legros

Narciso Legros

1620353636

Jenkins Log Analysis with LOGIQ

Jenkins is by far the leading open-source automation platform. A majority of developers turn to Jenkins to automate processes in their development, test, and deployment pipelines. Jenkins’ support for plugins helps automate nearly every task and set up robust continuous integration and continuous delivery pipelines.

Jenkins provides logs for every Job it executes. These logs offer detailed records related to a Job, such as a build name and number, time for completion, build status, and other information that help analyze the results of running the Job. A typical large-scale implementation of Jenkins in a multi-node environment with multiple pipelines generates tons of logs, making it challenging to identify errors and analyze their root cause(s) whenever there’s a failure. Setting up centralized observability for your Jenkins setup can help overcome these challenges by providing a single pane to log, visualize, and analyze your Jenkins logs. A robust observability platform enables you to debug pipeline failures, optimize resource allocation, and identify bottlenecks in your pipeline that hamper faster delivery.

#devops #log analysis #jenkins #jenkins logs

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Jenkins Log Analysis with LOGIQ
Narciso  Legros

Narciso Legros

1620353636

Jenkins Log Analysis with LOGIQ

Jenkins is by far the leading open-source automation platform. A majority of developers turn to Jenkins to automate processes in their development, test, and deployment pipelines. Jenkins’ support for plugins helps automate nearly every task and set up robust continuous integration and continuous delivery pipelines.

Jenkins provides logs for every Job it executes. These logs offer detailed records related to a Job, such as a build name and number, time for completion, build status, and other information that help analyze the results of running the Job. A typical large-scale implementation of Jenkins in a multi-node environment with multiple pipelines generates tons of logs, making it challenging to identify errors and analyze their root cause(s) whenever there’s a failure. Setting up centralized observability for your Jenkins setup can help overcome these challenges by providing a single pane to log, visualize, and analyze your Jenkins logs. A robust observability platform enables you to debug pipeline failures, optimize resource allocation, and identify bottlenecks in your pipeline that hamper faster delivery.

#devops #log analysis #jenkins #jenkins logs

Hyman  Simonis

Hyman Simonis

1620418680

Jenkins Log Analysis with LOGIQ

Jenkins is by far the leading open-source automation platform. A majority of developers turn to Jenkins to automate processes in their development, test, and deployment pipelines. Jenkins’ support for plugins helps automate nearly every task and set up robust continuous integration and continuous delivery pipelines.

Jenkins provides logs for every Job it executes. These logs offer detailed records related to a Job, such as a build name and number, time for completion, build status, and oth1er information that help analyze the results of running the Job. A typical large-scale implementation of Jenkins in a multi-node environment with multiple pipelines generates tons of logs, making it challenging to identify errors and analyze their root cause(s) whenever there’s a failure. Setting up centralized observability for your Jenkins setup can help overcome these challenges by providing a single pane to log, visualize, and analyze your Jenkins logs. A robust observability platform enables you to debug pipeline failures, optimize resource allocation, and identify bottlenecks in your pipeline that hamper faster delivery.

#log-analysis #jenkins

Jenkins Is Getting Old — It’s Time to Move On

By far, Jenkins is the most adopted tool for continuous integration, owning nearly 50% of the market share. As so many developers are using it, it has excellent community support, like no other Jenkins alternative. With that, it has more than 1,500 plugins available for continuous integration and delivery purposes.

We love and respect Jenkins. After all, it’s the first tool we encountered at the beginning of our automation careers. But as things are rapidly changing in the automation field, Jenkins is** left behind with his old approach**. Even though many developers and companies are using it, most of them aren’t happy with it. Having used it ourselves on previous projects, we quickly became frustrated by its lack of functionality, numerous maintenance issues, dependencies, and scaling problems.

We decided to investigate if other developers face the same problems and quickly saw the need to create a tool ourselves. We asked some developers at last year’s AWS Summit in Berlin about this. Most of them told us that they chose Jenkins because it’s free in the first place. However, many of them expressed interest in trying to use some other Jenkins alternative.

#devops #continuous integration #jenkins #devops adoption #jenkins ci #jenkins pipeline #devops continuous integration #jenkins automation #jenkins scripts #old technology

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer