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dbhosting · 4 years ago

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Which Is The Best PostgreSQL GUI? 2021 Comparison

PostgreSQL graphical user interface (GUI) tools help open source database users to manage, manipulate, and visualize their data. In this post, we discuss the top 6 GUI tools for administering your PostgreSQL hosting deployments. PostgreSQL is the fourth most popular database management system in the world, and heavily used in all sizes of applications from small to large. The traditional method to work with databases is using the command-line interface (CLI) tool, however, this interface presents a number of issues:

It requires a big learning curve to get the best out of the DBMS.

Console display may not be something of your liking, and it only gives very little information at a time.

It is difficult to browse databases and tables, check indexes, and monitor databases through the console.

Many still prefer CLIs over GUIs, but this set is ever so shrinking. I believe anyone who comes into programming after 2010 will tell you GUI tools increase their productivity over a CLI solution.

Why Use a GUI Tool?

Now that we understand the issues users face with the CLI, let’s take a look at the advantages of using a PostgreSQL GUI:

Shortcut keys make it easier to use, and much easier to learn for new users.

Offers great visualization to help you interpret your data.

You can remotely access and navigate another database server.

The window-based interface makes it much easier to manage your PostgreSQL data.

Easier access to files, features, and the operating system.

So, bottom line, GUI tools make PostgreSQL developers’ lives easier.

Top PostgreSQL GUI Tools

Today I will tell you about the 6 best PostgreSQL GUI tools. If you want a quick overview of this article, feel free to check out our infographic at the end of this post. Let’s start with the first and most popular one.

1. pgAdmin

pgAdmin is the de facto GUI tool for PostgreSQL, and the first tool anyone would use for PostgreSQL. It supports all PostgreSQL operations and features while being free and open source. pgAdmin is used by both novice and seasoned DBAs and developers for database administration.

Here are some of the top reasons why PostgreSQL users love pgAdmin:

Create, view and edit on all common PostgreSQL objects.

Offers a graphical query planning tool with color syntax highlighting.

The dashboard lets you monitor server activities such as database locks, connected sessions, and prepared transactions.

Since pgAdmin is a web application, you can deploy it on any server and access it remotely.

pgAdmin UI consists of detachable panels that you can arrange according to your likings.

Provides a procedural language debugger to help you debug your code.

pgAdmin has a portable version which can help you easily move your data between machines.

There are several cons of pgAdmin that users have generally complained about:

The UI is slow and non-intuitive compared to paid GUI tools.

pgAdmin uses too many resources.

pgAdmin can be used on Windows, Linux, and Mac OS. We listed it first as it’s the most used GUI tool for PostgreSQL, and the only native PostgreSQL GUI tool in our list. As it’s dedicated exclusively to PostgreSQL, you can expect it to update with the latest features of each version. pgAdmin can be downloaded from their official website.

pgAdmin Pricing: Free (open source)

2. DBeaver

DBeaver is a major cross-platform GUI tool for PostgreSQL that both developers and database administrators love. DBeaver is not a native GUI tool for PostgreSQL, as it supports all the popular databases like MySQL, MariaDB, Sybase, SQLite, Oracle, SQL Server, DB2, MS Access, Firebird, Teradata, Apache Hive, Phoenix, Presto, and Derby – any database which has a JDBC driver (over 80 databases!).

Here are some of the top DBeaver GUI features for PostgreSQL:

Visual Query builder helps you to construct complex SQL queries without actual knowledge of SQL.

It has one of the best editors – multiple data views are available to support a variety of user needs.

Convenient navigation among data.

In DBeaver, you can generate fake data that looks like real data allowing you to test your systems.

Full-text data search against all chosen tables/views with search results shown as filtered tables/views.

Metadata search among rows in database system tables.

Import and export data with many file formats such as CSV, HTML, XML, JSON, XLS, XLSX.

Provides advanced security for your databases by storing passwords in secured storage protected by a master password.

Automatically generated ER diagrams for a database/schema.

Enterprise Edition provides a special online support system.

One of the cons of DBeaver is it may be slow when dealing with large data sets compared to some expensive GUI tools like Navicat and DataGrip.

You can run DBeaver on Windows, Linux, and macOS, and easily connect DBeaver PostgreSQL with or without SSL. It has a free open-source edition as well an enterprise edition. You can buy the standard license for enterprise edition at $199, or by subscription at $19/month. The free version is good enough for most companies, as many of the DBeaver users will tell you the free edition is better than pgAdmin.

DBeaver Pricing

: Free community, $199 standard license

3. OmniDB

The next PostgreSQL GUI we’re going to review is OmniDB. OmniDB lets you add, edit, and manage data and all other necessary features in a unified workspace. Although OmniDB supports other database systems like MySQL, Oracle, and MariaDB, their primary target is PostgreSQL. This open source tool is mainly sponsored by 2ndQuadrant. OmniDB supports all three major platforms, namely Windows, Linux, and Mac OS X.

There are many reasons why you should use OmniDB for your Postgres developments:

You can easily configure it by adding and removing connections, and leverage encrypted connections when remote connections are necessary.

Smart SQL editor helps you to write SQL codes through autocomplete and syntax highlighting features.

Add-on support available for debugging capabilities to PostgreSQL functions and procedures.

You can monitor the dashboard from customizable charts that show real-time information about your database.

Query plan visualization helps you find bottlenecks in your SQL queries.

It allows access from multiple computers with encrypted personal information.

Developers can add and share new features via plugins.

There are a couple of cons with OmniDB:

OmniDB lacks community support in comparison to pgAdmin and DBeaver. So, you might find it difficult to learn this tool, and could feel a bit alone when you face an issue.

It doesn’t have as many features as paid GUI tools like Navicat and DataGrip.

OmniDB users have favorable opinions about it, and you can download OmniDB for PostgreSQL from here.

OmniDB Pricing: Free (open source)

4. DataGrip

DataGrip is a cross-platform integrated development environment (IDE) that supports multiple database environments. The most important thing to note about DataGrip is that it’s developed by JetBrains, one of the leading brands for developing IDEs. If you have ever used PhpStorm, IntelliJ IDEA, PyCharm, WebStorm, you won’t need an introduction on how good JetBrains IDEs are.

There are many exciting features to like in the DataGrip PostgreSQL GUI:

The context-sensitive and schema-aware auto-complete feature suggests more relevant code completions.

It has a beautiful and customizable UI along with an intelligent query console that keeps track of all your activities so you won’t lose your work. Moreover, you can easily add, remove, edit, and clone data rows with its powerful editor.

There are many ways to navigate schema between tables, views, and procedures.

It can immediately detect bugs in your code and suggest the best options to fix them.

It has an advanced refactoring process – when you rename a variable or an object, it can resolve all references automatically.

DataGrip is not just a GUI tool for PostgreSQL, but a full-featured IDE that has features like version control systems.

There are a few cons in DataGrip:

The obvious issue is that it’s not native to PostgreSQL, so it lacks PostgreSQL-specific features. For example, it is not easy to debug errors as not all are able to be shown.

Not only DataGrip, but most JetBrains IDEs have a big learning curve making it a bit overwhelming for beginner developers.

It consumes a lot of resources, like RAM, from your system.

DataGrip supports a tremendous list of database management systems, including SQL Server, MySQL, Oracle, SQLite, Azure Database, DB2, H2, MariaDB, Cassandra, HyperSQL, Apache Derby, and many more.

DataGrip supports all three major operating systems, Windows, Linux, and Mac OS. One of the downsides is that JetBrains products are comparatively costly. DataGrip has two different prices for organizations and individuals. DataGrip for Organizations will cost you $19.90/month, or $199 for the first year, $159 for the second year, and $119 for the third year onwards. The individual package will cost you $8.90/month, or $89 for the first year. You can test it out during the free 30 day trial period.

DataGrip Pricing

: $8.90/month to $199/year

5. Navicat

Navicat is an easy-to-use graphical tool that targets both beginner and experienced developers. It supports several database systems such as MySQL, PostgreSQL, and MongoDB. One of the special features of Navicat is its collaboration with cloud databases like Amazon Redshift, Amazon RDS, Amazon Aurora, Microsoft Azure, Google Cloud, Tencent Cloud, Alibaba Cloud, and Huawei Cloud.

Important features of Navicat for Postgres include:

It has a very intuitive and fast UI. You can easily create and edit SQL statements with its visual SQL builder, and the powerful code auto-completion saves you a lot of time and helps you avoid mistakes.

Navicat has a powerful data modeling tool for visualizing database structures, making changes, and designing entire schemas from scratch. You can manipulate almost any database object visually through diagrams.

Navicat can run scheduled jobs and notify you via email when the job is done running.

Navicat is capable of synchronizing different data sources and schemas.

Navicat has an add-on feature (Navicat Cloud) that offers project-based team collaboration.

It establishes secure connections through SSH tunneling and SSL ensuring every connection is secure, stable, and reliable.

You can import and export data to diverse formats like Excel, Access, CSV, and more.

Despite all the good features, there are a few cons that you need to consider before buying Navicat:

The license is locked to a single platform. You need to buy different licenses for PostgreSQL and MySQL. Considering its heavy price, this is a bit difficult for a small company or a freelancer.

It has many features that will take some time for a newbie to get going.

You can use Navicat in Windows, Linux, Mac OS, and iOS environments. The quality of Navicat is endorsed by its world-popular clients, including Apple, Oracle, Google, Microsoft, Facebook, Disney, and Adobe. Navicat comes in three editions called enterprise edition, standard edition, and non-commercial edition. Enterprise edition costs you $14.99/month up to $299 for a perpetual license, the standard edition is $9.99/month up to $199 for a perpetual license, and then the non-commercial edition costs $5.99/month up to $119 for its perpetual license. You can get full price details here, and download the Navicat trial version for 14 days from here.

Navicat Pricing

: $5.99/month up to $299/license

6. HeidiSQL

HeidiSQL is a new addition to our best PostgreSQL GUI tools list in 2021. It is a lightweight, free open source GUI that helps you manage tables, logs and users, edit data, views, procedures and scheduled events, and is continuously enhanced by the active group of contributors. HeidiSQL was initially developed for MySQL, and later added support for MS SQL Server, PostgreSQL, SQLite and MariaDB. Invented in 2002 by Ansgar Becker, HeidiSQL aims to be easy to learn and provide the simplest way to connect to a database, fire queries, and see what’s in a database.

Some of the advantages of HeidiSQL for PostgreSQL include:

Connects to multiple servers in one window.

Generates nice SQL-exports, and allows you to export from one server/database directly to another server/database.

Provides a comfortable grid to browse and edit table data, and perform bulk table edits such as move to database, change engine or ollation.

You can write queries with customizable syntax-highlighting and code-completion.

It has an active community helping to support other users and GUI improvements.

Allows you to find specific text in all tables of all databases on a single server, and optimize repair tables in a batch manner.

Provides a dialog for quick grid/data exports to Excel, HTML, JSON, PHP, even LaTeX.

There are a few cons to HeidiSQL:

Does not offer a procedural language debugger to help you debug your code.

Built for Windows, and currently only supports Windows (which is not a con for our Windors readers!)

HeidiSQL does have a lot of bugs, but the author is very attentive and active in addressing issues.

If HeidiSQL is right for you, you can download it here and follow updates on their GitHub page.

HeidiSQL Pricing: Free (open source)

Conclusion

Let’s summarize our top PostgreSQL GUI comparison. Almost everyone starts PostgreSQL with pgAdmin. It has great community support, and there are a lot of resources to help you if you face an issue. Usually, pgAdmin satisfies the needs of many developers to a great extent and thus, most developers do not look for other GUI tools. That’s why pgAdmin remains to be the most popular GUI tool.

If you are looking for an open source solution that has a better UI and visual editor, then DBeaver and OmniDB are great solutions for you. For users looking for a free lightweight GUI that supports multiple database types, HeidiSQL may be right for you. If you are looking for more features than what’s provided by an open source tool, and you’re ready to pay a good price for it, then Navicat and DataGrip are the best GUI products on the market.

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While I believe one of these tools should surely support your requirements, there are other popular GUI tools for PostgreSQL that you might like, including Valentina Studio, Adminer, DB visualizer, and SQL workbench. I hope this article will help you decide which GUI tool suits your needs.

Which Is The Best PostgreSQL GUI? 2019 Comparison

Here are the top PostgreSQL GUI tools covered in our previous 2019 post:

pgAdmin

DBeaver

Navicat

DataGrip

OmniDB

Original source: ScaleGrid Blog

#postgresql #postgres #gui #database #db

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greysnav · 3 years ago

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0 notes

programmerandcoder · 6 years ago

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Using 7 Mysql Strategies Like The Pros

MySQL gives you Let to host lots of databases and it is named by you. Using MySQL and PHPMyAdmin ( my favorite management GUI ) has enabled me to insource numerous solutions we used to cover.

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Almost any operating system and is operated in by mySQL Includes a controlled rate that is good I think it's the database manager together with all the rate of reaction to the procedures. Subqueries were one of the significant flaws of MySQL for quite a very long time; it had been notorious for dropping its way using a few degrees of sub-questions.

With MySQL, on the other hand, the Customer library is GPL, and that means you need to pay a commercial charge to Oracle or provide the source code of your program.PostgreSQL additionally supports data about data types, purposes and access methods from the system catalogs together with the typical information regarding databases, tables, and columns which relational databases maintain.

There are ways around the MySQL client library's licensing, the Route Atlassian decide to choose would be telling you where to get the JDBC connector out of for MySQL if you would like to join your Atlassian programs to a MySQL 38, and in which to drop the jar.

Seasoned staff if You'd like competently Accessible on-call assistance without paying serious cash ( DB2 or Oracle - degree paying ) Percona ( and MySQL ) is the friend. Matt Aslett of 451 Research unites ScaleBase to talk: scaling - outside of your MySQL DB, high availability strategies that are fresh, smartly managing a MySQL environment that is dispersed.

Conclusion Scalability is a matter of a theoretical Number of nodes It is also about the capacity to provide predictable performance And also to do this without adding management sophistication, proliferation of cloud, and geo-dispersed programs are adding to the sophistication MySQL hasn't been under so much strain that the mixture of innovative clustering/load balancing and management technology provides a possible solution ( c ) 2013 from The 451 Group.

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The dilemma is solved by database encryption, but Once the root accounts are compromised, it can't prevent access. You get rid of the ability of SQL, although application level encryption has become easily the most flexible and protected - it is pretty difficult to use columns in WHERE or JOIN clauses.

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Includes storage - engine frame that System administrators to configure the MySQL database for performance. Whether your system is Microsoft Linux, Macintosh or UNIX, MySQL is a solution that is comprehensive with self - handling features that automate all from configuration and space expansion to database management and information design.

By migrating database programs that are current to MySQL, businesses are currently enjoying substantial cost savings on jobs that are brand new. MySQL is an open source, multi-threaded, relational database management system ( RDBMS ) written in C and C++.

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MariaDB is a general - purpose DBMS engineered with extensible Structure to support a wide group of use cases through pluggable storage engines.MySQL users may get tens of thousands of metrics in the database, and so this guide we will concentrate on a small number of important metrics that will let you obtain real-time insight into your database wellbeing and functionality.

Users have a number of options for monitoring Latency, by taking advantage of MySQL's both built-in metrics and from querying the operation schema. The default storage engine, InnoDB of MySQL, utilizes an area of memory known as the buffer pool to indexes and tables.

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Blendo's ETL - as - a - service product makes it Simple to get data From several data sources such as S3 buckets, CSVs, and also a massive selection of third - party information sources such as Google Analytics, MailChimp, Salesforce and many others.

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MariaDB as the Number of storage motors and in excels Other plugins it ships together: Link and Cassandra storage motors for NoSQL backends or rolling migrations from legacy databases, Spider such as sharding, TokuDB with fractal indexes, etc.

MySQL is a relational database - Standard information schema also is composed of columns, tables, views, procedures, triggers, cursors, etc. MariaDB, therefore, has exactly the database structure and indicator and, on the other hand, is a branch of MySQL. Everything -- from the information, table definitions, constructions, and APIs -- stays identical when updating from MySQL into MariaDB.

MariaDB has experienced an increase in terms of Security features such as internal password and security management, PAM and LDAP authentication, Kerberos, user functions, and robust encryption within tablespaces, logs, and tables. MySQL can not do hash link or sort merge join - it merely can perform nested loops method that demands a lot of index lookups which might be arbitrary.

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For program, Example Hosts ( even though they need to be okay with attaining MySQL via proxies ), the proxy layer, and perhaps a management host. You ought to check of the logs and settings files and confirm that they're not readable by others.

Data may be moved between MySQL servers, For instance via MySQL replication that is regular or inside a Galera cluster. Flexibility is incorporating the features your company needs, although pushing arbitrary JSON seems elastic.

Among those enterprise qualities, Informix relational Databases, recently launched a new variant ( v12.10. XC2 ) which supports JSON / BSON info as a native from inside the relational database frame and fully supports each the MongoDB APIs so that any program is composed to the MongoDB, protocol may just be pointed in the Informix server and it'll just work.

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atlassianreleasenotes · 6 years ago

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Jira Service Desk 4.0.0 Release Notes

We're excited to present Jira Service Desk 4.0.

In our latest platform release, Jira Service Desk harnesses an upgraded engine that provides faster performance, increased productivity, and greater scalability. Dive in and discover all the goodies awaiting your service desk teams.

Highlights

Lucene upgrade

jQuery upgrade

Everyday tasks, take less time

Accessible priority icons

New options in advanced search

AUI upgrade

AdoptOpenJDK support

End of support for fugue

New customer portal in the works

Resolved issues

Get the latest version

Read the upgrade notes for important details about this release and see the full list of issuesresolved.

Compatible applications

If you're looking for compatible Jira applications, look no further:

Jira Software 8.0 release notes

Lucene upgrade to power performance

A welcome change we inherit from Jira 8.0, is the upgrade of Lucene. This change brings improvements to indexing, which makes it easier to administer and maintain your service desk. Here’s an overview of what to expect:

Faster reindexing

Reindexing is up to 11% faster. This means less time spent on reindexing after major configuration changes, and quicker upgrades.

Smaller indexes

Index size has shrunk by 47% (in our tests, the index size dropped from 4.5GB to 2.4GB). This means a faster, more stable, Jira that is easier to maintain and troubleshoot.

jQuery upgrade

Outdated libraries can lead to security vulnerabilities, so we've upgraded jQuery from version 1.7.2 to 2.2.4. This new version includes two security patches, and the jQuery migrate plugin 1.4.1 for a simplified upgrade experience. Learn more

Everyday tasks, take less time

We know how important it is for agents to move through their service desk with ease, and for admins to scale their service desk as their team grows. The back end upgrade combined with a bunch of front end improvements, makes work more efficient for admins, agents, and customers alike:

Viewing queues is twice as fast in 4.0 compared to 3.16.

Opening the customer page is 36% faster in 4.0 compared to 3.16

Project creation for an instance with 100 projects, is twice as fast in 4.0 compared to JSD 3.15.

Project creation for an instance with up to 200 projects, is six times faster in 4.0 compared to JSD 3.15.

We'll continue to take performance seriously, so that your service desk team can get the most out of their day!

Accessible priority icons

We’ve updated our priority icons to make them more distinctive and accessible. These changes allow Jira users with color blindness, to instantly recognize the relationship between icons, and their level of importance. Here’s a comparison of old and new icons that you'll be seeing around Jira Service Desk:

New options in advanced search

Check out these new options for when you are using advanced search:

Find contributors and date ranges (updatedBy)

Search for issues that were updated by a specific user, and within a specified date range. Whether you're looking for issues updated in the last 8 hours, two months, or between June and September 2017 – we've got you covered. Learn more

Find link types (issueLinkType)

Search for issues that are linked with other issues by particular link types, like blocks or is duplicated by. This will help you quickly find any related blockers, duplicates, and other issues that affect your work. Learn more

Atlassian User Interface (AUI) upgrade

AUI is a frontend library that contains all you need for building beautiful and functional Atlassian Server products and apps. We've upgraded AUI to the more modular 8.0, making it easier to use only the pieces that you need.

AdoptOpenJDK comes to Jira

Oracle stopped providing public updates for Oracle JDK 8 in January 2019. This means that only Oracle customers with a paid subscription or support contract will be eligible for updates.

In order to provide you with another option, we now support running Jira Service Desk 4.0 with AdoptOpenJDK 8. We'll continue to bundle Jira Service Desk with Oracle JDK / JRE 8.

End of support for fugue

In Jira Service Desk 4.0, we've removed com.atlassian.fugue, and updated our APIs to use Core Java Data types and Exceptions.

You can read the full deprecation notice for next steps, and if you have any questions – post them in the Atlassian Developer Community.

Get ready for the new customer portal experience

We're giving you more ways to customize your help center and portals, alongside a fresh new look that will brighten up your customer's day. This first-class, polished experience will be available in the not too distant future, so keep an eye on upcoming release notes. In the meantime, here’s a taster…

Added extras

4-byte characters

Jira now supports 4-byte characters with MySQL 5.7 and later. This means you can finally use all the emojis you've dreamed about!

Here's a guide that will help you connect Jira to a MySQL 5.7 database.

Add-ons are now apps

We're renaming add-ons to apps. This changed in our Universal Plugin Manager some time ago, and now Jira Service Desk follows suit. This change shouldn't really affect you, but we're letting you know so you're not surprised when seeing this new name in Jira administration, and other pages.

Issues in Release

Fixed: The SLA "within" field keeps increasing when the SLA is paused

Fixed: The SLA information is not included in the Excel and HTML export

Fixed: Anonymous comments cause display issues in Activity section in Issue Navigator

Fixed: Portal Settings - Highlight and Text on highlights values are swapped on subsequent edits

Fixed: Inconsistent JIRA Service Desk REST API Pagination

Fixed: JSON export doesn't differentiate public from internal comments

Fixed: Asynchronous cache replication queue - leaking file descriptor when queue file corrupted

Fixed: New line character in issue summary can break issue search detailed view

Fixed: Jira incorrectly sorts options from Select List custom fields with multiple contexts

Fixed: DefaultShareManager.isSharedWith performs user search 2x times

Fixed: Unknown RPC service: update_security_token

Fixed: Copying of SearchRequest performs user search

Fixed: Data Center - Reindex snapshot file fails to create if greater than 8GB

Fixed: The VerifyPopServerConnection resource was vulnerable to SSRF - CVE-2018-13404

Fixed: XSS in the two-dimensional filter statistics gadget on a Jira dashboard - CVE-2018-13403

Fixed: DC index copy does not clean up old files when Snappy archiver is used

Fixed: SQL for checking usages of version in custom fields is slow even if no version picker custom fields exist

Suggestion: Improve FieldCache memory utilisation for Jira instances with large Lucene

Fixed: Search via REST API might fail due to ClassCastException

Fixed: Upgrade Tomcat to the version 8.5.32

Suggestion: Webhook for Project Archive on JIRA Software

Fixed: Deprecate support for authenticating using os_username, os_password as url query parameters

Fixed: JQL input missing from saved filter

Fixed: CachingFieldScreenStore unnecessary flushes fieldScreenCache for create/remove operation

Suggestion: Favourite filters missing in Mobile browser view

Fixed: DC node reindex is run in 5s intervals instead of load-dependent intervals

Suggestion: Don't log Jira events to STDOUT - catalina.out

Fixed: Adding .trigger-dialog class to dropdown item doesn't open a dialog

Fixed: Issue view/create page freeze

Suggestion: Add Additional Logging Related to Index Snapshot Backup

Suggestion: Add additional logging related to index copy between nodes

Fixed: JIRA performance is impacted by slow queries pulling data from the customfieldvalue table

Fixed: Projects may be reverted to the default issue type scheme during the change due to race condition

Suggestion: As an JIRA Datacenter Administrator I want use default ehcache RMI port

Fixed: Two dimensional filter statistics gadget does not show empty values

Suggestion: Embed latest java critical security update (1.8.0.171 or higher) into the next JIRA (sub)version

Fixed: JIRA inefficiently populates fieldLayoutCache due to slow loading of FieldLayoutItems

Suggestion: Provide New look Of Atlassian Product For Server Hosting As Well

Fixed: Startup Parameter "upgrade.reindex.allowed" Not Taking Effect

Fixed: Remote Linking of Issues doesn't reindex issues

Fixed: Index stops functioning because org.apache.lucene.store.AlreadyClosedException is not handled correctly

Suggestion: Add Multi-Column Index to JIRA Tables

Fixed: 'entity_property' table is slow with high number of rows and under high load

Fixed: JIRA Data Center will skip replication operations in case of index exception

Fixed: Improve database indexes for changeitem and changegroup tables.

Suggestion: CSV export should also include SLA field values

Fixed: Priority icons on new projects are not accessible for red-green colour blind users

Fixed: Two Dimensional Filter Statistics Gadget fails when YAxis is a Custom Field restricted to certain issue types

Fixed: Unable to remove user from Project Roles in project administration when the username starts with 0

Suggestion: The jQuery version used in JIRA needs to be updated

Fixed: MSSQL Selection in config.sh Broken (CLI)

Suggestion: Upgrade Lucene to version 7.3

Fixed: User Directory filter throws a 'value too long' error when Filter exceeds 255 chars

Suggestion: Add support for 4 byte characters in MySQL connection

Fixed: xml view of custom field of multiversion picker type display version ID instead of version text

Fixed: Filter gadgets take several minutes to load after a field configuration context change

Suggestion: Ship JIRA with defaults that enable log rotation

Fixed: The xml for version picker customfield provides the version id instead of the version name

Suggestion: JQL function for showing all issues linked to any issue by a given issue link type

Suggestion: Ability to search for issues with blockers linked to them

Suggestion: Reduce JIRA email chatiness

Source

#jira #jiraservicedesk #jiraserver #atlassian #atlassianreleasenotes

0 notes

uplatz-blog · 6 years ago

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ORACLE BI PUBLISHER 12C R1ABOUT THIS COURSEThe Oracle BI Publisher 12c training teaches you the fundamental concepts of Oracle BI Publisher, a reporting & document output management solution. Develop skills and design reports by using components such as Data Model, Report and Layout Editors.

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Course Agenda

Functions of Reporting Systems including Challenges

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BI Publisher as a Strategic Reporting Solution for All Applicationss

Overview of Oracle BI Foundation Suite

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Overview of Oracle BI EE

Overview of Oracle Fusion Middleware

BI Publisher Technology and Architecture

Multitier Architecture

Internationalization and Language Support

Supported Data Sources

Layout Templates

Document Generation Process and Output Formats

Bursting Overview

Functional Components

Enterprise Server Architecture and Performance and Scalability

Getting Started with BI Publisher

Managing Repository Objects

Viewing Reports

Configuring Report Properties

Using Create Report wizard to Create Reports

Selecting Data: Data Model, Spreadsheet, and BI Subject Area

Logging In, the Home Page, and Global Header, and Setting Account Preferences

Managing Favorites

Using Oracle Business Intelligence (BI) Publisher

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Describing the Vision Charts of Accounts

Explaining the Vision Story

Navigating Oracle Cloud Applications

Locating Course Resources

Running an Oracle BI Publisher Report from the Financial Reporting Center

Oracle Business Intelligence Publisher (BI) Fundamentals

Describing the Components of Oracle BI Publisher

Defining Templates Using the Word Template Builder

Understanding and Using the BI Publisher Customize Feature

Creating and Editing Report Layouts Using the Layout Editor

Understanding the Report Configuration Options

Creating a New Report Using an Excel Spreadsheet as the Data Source

Using the Data Model Editor

Viewing Data and Saving Sample Data Sets

Configuring Parameter Settings and Viewing Reports with Parameters

Adding Parameters and LOVs to the Query

Creating a Simple Data Model based on a SQL Query Data Set

Exploring the Data Model Editor UI and the Supported Data Sources

Exploring the Schemas Used in the Course

Creating a Private Data Source

Using Query Builder to Build a Query

Working with Layout Editor

Creating Boilerplates

Working with Lists, Gauges and Pivot Tables

Creating a Layout by Using a Basic Template

Inserting a Layout Grid

Adding a Table, Formatting Columns, Defining Sorts and Groups, and Applying Conditional Formats

Adding Repeating Sections, Text Items, and Images

Opening the Layout Editor and Navigating the Layout Editor UI

Inserting and Editing Charts, and Converting Charts to a Pivot Tables

Using Template Builder to Create RTF Templates

Exploring the Basic and Form Field Methods

Creating an RTF Template from a Sample, Changing Field Properties, and Previewing Table Data

Designing an RTF Template for a BI Publisher Report

Adding a Chart to an RTF Template

Creating a BI Publisher Report by Using Template Builder in Online Mode

Using the BI Publisher Menu Bar

Exploring Advanced RTF Template Techniques Including Conditional Formats, Watermarks, Page-Level Calculations, Running Totals, Grouping, and Sorting

BI Publisher Server: Administration and Security

Describing Groups, Users, Roles, and Permissions

Integrating with Oracle BI Presentation Services and Oracle Endeca Server

Describing Delivery Options Including Print, Fax, Email, WebDav, HTTP Server, FTP, and CUPS

Describing the Security Model for BI Publisher and Oracle Fusion Middleware

Creating the JDBC Connections

Setting, Viewing, and Updating Data Sources

Describing and Configuring BI Publisher Scheduler

Describing the Administration Page

Scheduling and Bursting Reports

Adding a Bursting Definition to a Data Model

Managing and Viewing a Report Job

Scheduling a Bursting Job

Viewing Report Job History

Scheduling a Report with Trigger

Describing Bursting

Scheduling and Describing a Report Job and Related Options

Integrating BI Publisher with Oracle BI Enterprise Edition

Configuring Presentation Services Integration

Creating a Data Model and Report based on a BI Server SQL Query

Navigating Oracle BI EE

Adding a BI Publisher Report to an Oracle BI EE Dashboard

Creating a Data Model and Report based on an Oracle BI Analysis

Creating a Report based on OBI EE Subject Area

Creating Data Models and BI Publisher Reports Based on Other Data Sources

Explaining Proxy Setting for Web Services and HTTP Data Sources

Creating a BI Publisher Report based on an External Web Service

Creating a BI Publisher Report Based on CSV Data source

Describing the Web Services Data Source

Configuring Presentation Services Integration

Creating a BI Publisher Report based on an HTTP Data Set

Describing the HTTP (XML/RSS Feed) Data Source

Creating a BI Publisher Report Based on XML File

Performing Translations

Exporting and Importing the XLIFF for a Catalog Folder

Managing XLIFF Translations on BI Publisher Server

Describing the Overall Translation Process

Translating by Using the XLIFF Option

Translating by Using the Localized Template Option

Describing Catalog Translation

Describing Translation Types.

For any questions, simply contact us at -

Call: +44 7836 212635 WhatsApp: +44 7836 212635 Email: [email protected] https://training.uplatz.com

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foxpeople477 · 4 years ago

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Web Scraping With Django

In this tutorial, we are going to learn about creating Django form and store data into the database. The form is a graphical entity on the website where the user can submit their information. Later, this information can be saved in the database and can be used to perform some other logical operation. Hi,Greetings for the day I have deep knowledge web scraping. Feel free to contact me. I am Python and Website developer I worked on the below technologies: Back End: - Python with Django and Flask Framework - RE More.

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In August this year, Django 3.1 arrived with support for Django async views. This was fantastic news but most people raised the obvious question – What can I do with it? There have been a few tutorials about Django asynchronous views that demonstrate asynchronous execution while calling asyncio.sleep. But that merely led to the refinement of the popular question – What can I do with it besides sleep-ing?

The short answer is – it is a very powerful technique to write efficient views. For a detailed overview of what asynchronous views are and how they can be used, keep on reading. If you are new to asynchronous support in Django and like to know more background, read my earlier article: A Guide to ASGI in Django 3.0 and its Performance.

Django Async Views

Django now allows you to write views which can run asynchronously. First let’s refresh your memory by looking at a simple and minimal synchronous view in Django:

It takes a request object and returns a response object. In a real world project, a view does many things like fetching records from a database, calling a service or rendering a template. But they work synchronously or one after the other.

Web Scraping With Django Using

In Django’s MTV (Model Template View) architecture, Views are disproportionately more powerful than others (I find it comparable to a controller in MVC architecture though these things are debatable). Once you enter a view you can perform almost any logic necessary to create a response. This is why Asynchronous Views are so important. It lets you do more things concurrently.

It is quite easy to write an asynchronous view. For example the asynchronous version of our minimal example above would be:

This is a coroutine rather than a function. You cannot call it directly. An event loop needs to be created to execute it. But you do not have to worry about that difference since Django takes care of all that.

Note that this particular view is not invoking anything asynchronously. If Django is running in the classic WSGI mode, then a new event loop is created (automatically) to run this coroutine. Holy panda switch specs. So in this case, it might be slightly slower than the synchronous version. But that’s because you are not using it to run tasks concurrently.

So then why bother writing asynchronous views? The limitations of synchronous views become apparent only at a certain scale. When it comes to large scale web applications probably nothing beats FaceBook.

Views at Facebook

In August, Facebook released a static analysis tool to detect and prevent security issues in Python. But what caught my eye was how the views were written in the examples they had shared. They were all async!

Note that this is not Django but something similar. Currently, Django runs the database code synchronously. But that may change sometime in the future.

If you think about it, it makes perfect sense. Synchronous code can be blocked while waiting for an I/O operation for several microseconds. However, its equivalent asynchronous code would not be tied up and can work on other tasks. Therefore it can handle more requests with lower latencies. More requests gives Facebook (or any other large site) the ability to handle more users on the same infrastructure.

Even if you are not close to reaching Facebook scale, you could use Python’s asyncio as a more predictable threading mechanism to run many things concurrently. A thread scheduler could interrupt in between destructive updates of shared resources leading to difficult to debug race conditions. Compared to threads, coroutines can achieve a higher level of concurrency with very less overhead.

Misleading Sleep Examples

As I joked earlier, most of the Django async views tutorials show an example involving sleep. Even the official Django release notes had this example:

To a Python async guru this code might indicate the possibilities that were not previously possible. But to the vast majority, this code is misleading in many ways.

Firstly, the sleep happening synchronously or asynchronously makes no difference to the end user. The poor chap who just opened the URL linked to that view will have to wait for 0.5 seconds before it returns a cheeky “Hello, async world!”. If you are a complete novice, you may have expected an immediate reply and somehow the “hello” greeting to appear asynchronously half a second later. Of course, that sounds silly but then what is this example trying to do compared to a synchronous time.sleep() inside a view?

The answer is, as with most things in the asyncio world, in the event loop. If the event loop had some other task waiting to be run then that half second window would give it an opportunity to run that. Note that it may take longer than that window to complete. Cooperative Multithreading assumes that everyone works quickly and hands over the control promptly back to the event loop.

Secondly, it does not seem to accomplish anything useful. Some command-line interfaces use sleep to give enough time for users to read a message before disappearing. But it is the opposite for web applications - a faster response from the web server is the key to a better user experience. So by slowing the response what are we trying to demonstrate in such examples?

The best explanation for such simplified examples I can give is convenience. It needs a bit more setup to show examples which really need asynchronous support. That’s what we are trying to explore here.

Better examples

A rule of thumb to remember before writing an asynchronous view is to check if it is I/O bound or CPU-bound. A view which spends most of the time in a CPU-bound activity for e.g. matrix multiplication or image manipulation would really not benefit from rewriting them to async views. You should be focussing on the I/O bound activities.

Invoking Microservices

Most large web applications are moving away from a monolithic architecture to one composed of many microservices. Rendering a view might require the results of many internal or external services.

In our example, an ecommerce site for books renders its front page - like most popular sites - tailored to the logged in user by displaying recommended books. The recommendation engine is typically implemented as a separate microservice that makes recommendations based on past buying history and perhaps a bit of machine learning by understanding how successful its past recommendations were.

In this case, we also need the results of another microservice that decides which promotional banners to display as a rotating banner or slideshow to the user. These banners are not tailored to the logged in user but change depending on the items currently on sale (active promotional campaign) or date.

Let’s look at how a synchronous version of such a page might look like:

Here instead of the popular Python requests library we are using the httpx library because it supports making synchronous and asynchronous web requests. The interface is almost identical.

The problem with this view is that the time taken to invoke these services add up since they happen sequentially. The Python process is suspended until the first service responds which could take a long time in a worst case scenario.

Let’s try to run them concurrently using a simplistic (and ineffective) await call:

Notice that the view has changed from a function to a coroutine (due to async def keyword). Also note that there are two places where we await for a response from each of the services. You don’t have to try to understand every line here, as we will explain with a better example.

Interestingly, this view does not work concurrently and takes the same amount of time as the synchronous view. If you are familiar with asynchronous programming, you might have guessed that simply awaiting a coroutine does not make it run other things concurrently, you will just yield control back to the event loop. The view still gets suspended.

Let’s look at a proper way to run things concurrently:

If the two services we are calling have similar response times, then this view should complete in _half _the time compared to the synchronous version. This is because the calls happen concurrently as we would want.

Let’s try to understand what is happening here. There is an outer try…except block to catch request errors while making either of the HTTP calls. Then there is an inner async…with block which gives a context having the client object.

The most important line is one with the asyncio.gather call taking the coroutines created by the two client.get calls. The gather call will execute them concurrently and return only when both of them are completed. The result would be a tuple of responses which we will unpack into two variables response_p and response_r. If there were no errors, these responses are populated in the context sent for template rendering.

Microservices are typically internal to the organization hence the response times are low and less variable. Yet, it is never a good idea to rely solely on synchronous calls for communicating between microservices. As the dependencies between services increases, it creates long chains of request and response calls. Such chains can slow down services.

Why Live Scraping is Bad

We need to address web scraping because so many asyncio examples use them. I am referring to cases where multiple external websites or pages within a website are concurrently fetched and scraped for information like live stock market (or bitcoin) prices. The implementation would be very similar to what we saw in the Microservices example.

But this is very risky since a view should return a response to the user as quickly as possible. So trying to fetch external sites which have variable response times or throttling mechanisms could be a poor user experience or even worse a browser timeout. Since microservice calls are typically internal, response times can be controlled with proper SLAs.

Ideally, scraping should be done in a separate process scheduled to run periodically (using celery or rq). The view should simply pick up the scraped values and present them to the users.

Serving Files

Django addresses the problem of serving files by trying hard not to do it itself. This makes sense from a “Do not reinvent the wheel” perspective. After all, there are several better solutions to serve static files like nginx.

But often we need to serve files with dynamic content. Files often reside in a (slower) disk-based storage (we now have much faster SSDs). While this file operation is quite easy to accomplish with Python, it could be expensive in terms of performance for large files. Regardless of the file’s size, this is a potentially blocking I/O operation that could potentially be used for running another task concurrently.

Imagine we need to serve a PDF certificate in a Django view. However the date and time of downloading the certificate needs to be stored in the metadata of the PDF file, for some reason (possibly for identification and validation).

We will use the aiofiles library here for asynchronous file I/O. The API is almost the same as the familiar Python’s built-in file API. Here is how the asynchronous view could be written:

This example illustrates why we need asynchronous template rendering in Django. But until that gets implemented, you could use aiofiles library to pull local files without skipping a beat.

There are downsides to directly using local files instead of Django’s staticfiles. In the future, when you migrate to a different storage space like Amazon S3, make sure you adapt your code accordingly.

Handling Uploads

On the flip side, uploading a file is also a potentially long, blocking operation. For security and organizational reasons, Django stores all uploaded content into a separate ‘media’ directory.

If you have a form that allows uploading a file, then we need to anticipate that some pesky user would upload an impossibly large one. Thankfully Django passes the file to the view as chunks of a certain size. Combined with aiofile’s ability to write a file asynchronously, we could support highly concurrent uploads.

Again this is circumventing Django’s default file upload mechanism, so you need to be careful about the security implications.

Where To Use

Django Async project has full backward compatibility as one of its main goals. So you can continue to use your old synchronous views without rewriting them into async. Asynchronous views are not a panacea for all performance issues, so most projects will still continue to use synchronous code since they are quite straightforward to reason about.

In fact, you can use both async and sync views in the same project. Django will take care of calling the view in the appropriate manner. However, if you are using async views it is recommended to deploy the application on ASGI servers.

This gives you the flexibility to try asynchronous views gradually especially for I/O intensive work. You need to be careful to pick only async libraries or mix them with sync carefully (use the async_to_sync and sync_to_async adaptors).

Disney plus on switch. Hopefully this writeup gave you some ideas.

Web Development With Django

Thanks to Chillar Anand and Ritesh Agrawal for reviewing this post. All illustrations courtesy of Old Book Illustrations

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lindadennisblog · 7 years ago

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nyumintelligent-blog · 8 years ago

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