How to Enable/Disable Developer mode in Android phones

How to Enable/Disable Developer mode in Android phones

1.Go to Android device’s “Settings”,
2.Then go to “General” settings- please verify some phones may vary with flow, I am referring LG G2 phone’s navigation flow.
3.Scroll down to see “About Phone”
4.In About phone, single tap on “Software info” you would see “Build number”
5.Then, tap on 7 times on “Build number”to enable Developer mode or options.

Note: if the phone is already in Developer mode, then you can directly navigate to
Settings ==> About Phone ==> Developer options ==> enable/disable check box of USB Debugging.

Performance test analysis.

Performance test analysis.

What is Performance test analysis?

Performance test analysis can be done real time script execution or post execution

 Real time script execution analysis

1)      Performance Engineer needs a coordination  with other teams like Database team, app server team, etc  to monitor real time performance on Database server, Application server

2)      Database and app server team can let Performance Engineer know the performance results on these servers like resource utilization, fail over server works / load balancing works fine etc.

Post script execution analysis

1)      Verification on Vuser metrics, Vuser errors,

2)      Verification Transactions graphs for TPS, response time, hits per second etc.

For additional details please click on click link https://shalimatech.com/load-testing-vs-performance-testing-vs-stress-testing/

vlookup function in excel for column value matching

vlookup function in excel for column value matching

Row# 2 and column A showed the match results for Column B Row# 1 as you can see value 1 is matched against Column C records.

Row# 4 and column A showed no match results for Column B Row# 4 as you can see value 5A is not matched against Column C records.

so the code sample you need to use for the record range of 11 rows, as below

=IF(ISNA(VLOOKUP(B2, $C$2:$C$11, 2, FALSE )), “MISSING”, “MATCHED!”)

=IF(ISNA(VLOOKUP(B3, $C$2:$C$11, 2, FALSE )), “MISSING”, “MATCHED!”)

etc

VLOOKUP

 

 

Performance testing best practices and activities.

Performance testing best practices and activities.

  1. As first step, Performance Engineer needs to learn the requirements, performance expectations & system architecture.

Note: To learn the performance requirements & expectations, SME (Subject matter expert ) or production user can help. This information will help to define performance goals and SLAs

  1. Then develop best matching performance test scenarios, approach, define goals (SLAs) & sign off test plan.

Note 1: To identify the right performance test scenarios, SME can give some feedback on about the current performance issues in production / live application that he/she is facing.

Note 2:  You can refer another blog of mine on different types of performance scenarios – click link load testing vs performance testing vs stress testing

  1. Now Performance Engineer can developer V– USER scripts and execute the scripts in Controller to generate performance analysis results

Note 1: Apart from delivering performance analysis results, Performance Engineer  can coordinate with other team to monitor real time performance on Database server, Application server and also make sure load balancing is working fine while stress testing.

Some of the e.g. when performance testing is needed

  1. for Database upgrade,
  2. Application upgrade,
  3. Or if user complains that application performance is bad – like page response is not meeting his/her expectations.

Robot framework and Selenium2Library

Robot framework and Selenium2Library

Selenium2Library is library created for web testing. Robot framework utilizes this framework

Robot framework is mainly used for acceptance testing and it supports different browsers like IE, Chrome, Firefox etc.

Robot framework can be hosted in Github and script execution can be managed using Jenkins. I will be writing a detailed step by step documentation on Robot framework web testing  implementation.

Jenkins vs GitHub

Jenkins vs GitHub

Jenkins allow continuous integration which helps developers or Automation testers to integrate code into a shared repository. It acts an open source automation server for test automation projects. Jenkins offers lots of plugins to integrate various tools.

GitHub is a web-based hosting service supporting distributed version control of codes. for e.g. if someone already registered a domain for a website in GoDaddy or similar domain services site but looking for a free hosting service, Github can be used to store code. Go daddy and Github should be linked to make this happen.

What is Structured Query Language(SQL)

What is Structured Query Language(SQL)

SQL is Structured Query Language. e.g.MySQL and Microsoft SQL Server both are relational database management systems)
Data Definition L (DDL) – Create, Alter, Drop
Data Manipulation Language (DML)- Select , Insert, Update, Delete
Data Control Language(DCL) – Grant, Revoke

Apache JMeter features and uses:

Apache JMeter features and uses:

Apache JMeter is a performance tool developed in Java.

Supports both load and performance tests,
Supports different applications/server/protocol types,
Web – HTTP, HTTPS  –  i.e application made in Java, NodeJS, PHP, ASP.NET etc
SOAP / REST Webservices, FTP , LDAP & TCP etc.

Additionally it supports Database via JDBC, Message-oriented middleware  via JMS, Mail – SMTP(S), POP3(S) and IMAP(S), also Native commands or shell scripts & Java Objects

Hadoop Big Data quick summary

Hadoop Big Data quick summary

Hadoop – is a Java based programming framework that supports the processing of large data sets in a distributed computing environment
Hadoop – is based on Google File System (GFS)
Hadoop – uses thousands of nodes this is the key to improve performance.
Hadoop – is a Distributed File System or HDFS, which enables fast data transfer among the nodes.
Hadoop Configuration – has got the three modes of Hadoop configuration – Standalone, pseudo distributed, and fully distributed.
Hadoop MapReduce – Hadoop MapReduce is the core components of Hadoop and is a programming model and helps implementation for processing and generating large data sets, it uses parallel and distributed algorithms on a cluster. it can handle large scale data: petabytes, exabytes.
Mapreduce framework converts each record of input into a key/value pair.
Ubuntu Server – Ubuntu is a leading open-source platform. it helps in utilizing the infrastructure to users when they want to deploy a cloud, a web farm, or a Hadoop cluster.
HadoopDistributed File System (HDFS)- HadoopDistributed File System (HDFS) is a block-structured, distributed file system.
Distributed Cache – Distributed Cache is a Hadoop feature that helps cache files needed by applications.

Pig – is an Apache open-source project and one of the components of the Hadoop eco-system.
Pig – is a high-level data flow scripting language and runs on the Hadoopclusters.
Pig – uses HDFS for storing and retrieving data and Hadoop MapReduce for processing Big Data.

Hive – is a data warehouse system for Hadoop.
Hive – facilitates ad hoc queries and aids analysis of data sets stored in Hadoop.
Hive – provides an SQL like language called HiveQL(HQL)

Apache HBase – is a distributed, column oriented database.
Apache HBase – is built on top of HDFS.
Apache HBase – is an open-source, distributed, versioned, non relational database system.
Apache HBase – has two types of Nodes. 1. Master and 2. Region Server.

Cloudera – is a commercial vendor for deploying Hadoopin an enterprise.
Cloudera – offers ClouderaManager for system management, ClouderaNavigator for data management.

ZooKeeper – is an open source and high performance co ordination service for distributed applications.

Pivotal HD – is a commercially supported, enterprise capable distribution of Hadoop and it aims to accelerate data analytics projects.

Sqoop – Sqoop is an Apache Hadoop ecosystem project. Sqoop’s responsibility is to import or export operations across relational databases.

Apache Oozie – is a workflow scheduler system used to manage Apache Hadoop jobs/MapReduce jobs

Mahout – is library of machine learning algorithams, helps in clustering and Clustering allows the system to group various entities into separate clusters or groups based on certain characteristics or features.

Apache Cassandra – Apache Cassandra is an open source, freely distributed, high-performance, extremely scalable, and fault-tolerant post relational database.
Apache Spark – is a powerfull open source processing engine and general MapReduce like engine used for large-scale data processing.

Apache Ambari – Apache Ambari is a completely open operational tool or framework for provisioning, managing, and monitoring Apache Hadoop clusters.
Kerberos – is a third party authentication mechanism. It has a database of the users/services and their respective Kerberos passwords.

Java quick reference – Please click here