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Access VSO Work Items in Postman, by using Azure DevOps REST APIs, using PAT (Personal Access Token) authentication

March 4, 2021 1 comment

 
Here in this post we will try to access VSO Work Items from Postman tool by using Azure DevOps REST APIs. To access and authenticate into Azure DevOps we will first create PAT (Personal Access Token) and then use it in the Postman tool.
 

Create Token in Azure DevOps portal:

 

1. Go to https://dev.azure.com/{YourOrg}/. On the top-right corner of the browser click on “User Settings” icon, and select “Personal access token” option from the drop down.


 

2. Now on the settings page click on the + icon to create a new Token, provide a new Name and Expiry Date of the Token, and scroll down.


 

3. At the bottom of the page you will see “Work Items” section, just select the “Read & write” check box, and click Create.


 

4. Now your Token is created, just copy it by clicking on the Copy button and save it somewhere securely. Otherwise you won’t be seeing it again on the portal for security reasons.


 

Using Postman to access VSO Work Items:

 

5. Download & Install Postman from here [link]
 

6. Open Postman tool, create a new Collection and add a new Request of type GET.

Populate following URL: https://dev.azure.com/{{YourOrg}}/{{YourProject}}/_apis/wit/workitems/
{{workitemId}}?api-version=6.0

Auth Tab:
  — Type = “Basic Auth”
    — UserName: {{PAT name}}
    — Password: {{Copied PAT secret from Azure DevOps portal}}


 

6. Now clicking on Send button will return you the Response with all the VSO Work Item attributes and details.


REST API, PATCH request error, Postman – Valid content types for this method are: application/json-patch+json

March 2, 2021 Leave a comment

 
I was trying to use the Azure DevOps REST APIs to get some details of VSO WorkItems by using Postman tool. Doing GET-Request was not a problem and it returned the expected JSON response body with all attributes and details.

But when I tried to update the same VSO WorkItem by using POST request it resulted in below error:

{
“$id”: “1”,
“innerException”: null,
“message”: “The request indicated a Content-Type of \”application/json\” for method type \”PATCH\” which is not supported. Valid content types for this method are: application/json-patch+json.“,
“typeName”: “Microsoft.VisualStudio.Services.WebApi.VssRequestContentTypeNotSupportedException, Microsoft.VisualStudio.Services.WebApi”,
“typeKey”: “VssRequestContentTypeNotSupportedException“,
“errorCode”: 0,
“eventId”: 3000
}

 

Solution:

As the above error mentions to use “application/json-patch+json” as the Content Type, so the same has to be added under the Headers tab as Key Value, like:

KEY: Content-Type
VALUE: application/json-patch+json


 

If you observe similar error while working with PowerShell, you’ve to pass the same value with ContentType parameter in the Invoke-RestMethod command:

Invoke-RestMethod -Method Post -Uri $theUri -Headers $theHeader -ContentType “application/json-patch+json” -Body $theBody


Database backup to Azure blob storage with SQL Server 2016+

February 9, 2021 Leave a comment

 
Backup to Azure was made available with SQL Server 2012 SP1 CU2. It provided significant cost savings versus on-premises costs of onsite/offsite storage, and device maintenance and better scalability than logical drives connected to Azure machines.

But backup to Azure was comparatively slow, and the maximum backup size was 1 TB, till SQL Server 2014.

 
Now with SQL Server 2016+ backup to block blobs offers more cost-effective storage, performance increases with backup striping and a faster restore process, support for larger backups, up to 12 TB, as well as granular access and a unified credential story.

Backup to block blobs also supports all of the existing backup and restore features, with the exception that appends are not supported.
 


 

For detailed information please check the Quickstart guide to SQL backup and restore to Azure Blob storage service


2020 blogging in review (Thank you & Happy New Year 2021 !!!)

December 31, 2020 Leave a comment

 

Happy New Year 2021… from SQL with Manoj !!!

As WordPress.com Stats helper monkeys have stopped preparing annual report from last few years for the blogs hosted on their platform. So I started preparing my own Annual Report every end of the year to thank my readers for their support, feedback & motivation, and also to check & share the progress of this blog.
 

In year 2019 & 2020 I could not dedicated much time here, so there were very few blogs posted by me. In mid 2019 something strange happened and my blog hits started declining day by day. Usually I used to get daily ~3.5k hits and within few months hits were reduced to just half. As the daily hits were under ~1.5k so I checked the SEO section in WordPress-admin, and I was surprised to discover that my blog & meta info was missing in Google webmaster.

I re-entered the meta info and since last 1 year the blog hits are stable at ~1k hits per day which is very low from what I was getting ~2 years back. Thus, you can see a drastic decline of hits in 2020 year below.

 
–> Here are some Crunchy numbers from 2020

The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 281,285 times by 213,447 unique visitors in 2020. If it were an exhibit at the Louvre Museum, it would take about 40 days for that many people to see it.

There were 22 pictures uploaded, taking up a total of ~1 MB. That’s about ~2 pictures every month.

 

–> All-time posts, views, and visitors

SQL with Manoj all time views


 

–> Posting Patterns

In 2020, there were 10 new posts, growing the total archive of this blog to 561 posts.

LONGEST STREAK: 4 post in Oct 2020

 

–> Attractions in 2020

These are the top 5 posts that got most views in 2020:

0. Blog Home Page (49,287 views)

1. The server may be running out of resources, or the assembly may not be trusted with PERMISSION_SET = EXTERNAL_ACCESS or UNSAFE (12,870 views)

2. Windows could not start SQL Server, error 17051, SQL Server Eval has expired (8,498 views)

3. Reading JSON string with Nested array of elements (8,016 views)

4. SQL Server 2016 RTM full and final version available (7,714 views)

5. Windows could not start SQL Server (moved Master DB) (6,988 views)

 

–> How did they find me?

The top referring sites and search engines in 2020 were:

SQL with Manoj 2020 Search Engines and other referrers

 

–> Where did they come from?

Out of 210 countries, top 5 visitors came from United States, India, United Kingdom, Canada and Australia:

SQL with Manoj 2020 top Countries visitors

 

–> Followers: 442

WordPress.com: 180
Email: 262
Facebook Page: 1,480

 

–> Alexa Rank (lower the better)

Global Rank: 835,163 (as of 31st DEC 2020)
Previous rank: 221,534 (back in 2019)

 

–> YouTube Channel:

SQLwithManoj on YouTube
– Total Subscribers: 17,1700
– Total Videos: 70

 

–> 2021 New Year Resolution

– Write at least 1 blog post every week
– Write on new features in SQL Server 2019
– I’ve also started writing on Microsoft Big Data Platform, related to Azure Data Lake and Databricks (Spark/Scala), so I will continue to explore more on this area.
– Post at least 1 video every week on my YouTube channel

 

That’s all for 2020, see you in year 2021, all the best !!!
 

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Apache Spark – new Features & Improvements in Spark 3.0

October 27, 2020 1 comment


 
With Spark 3.0 release (on June 2020) there are some major improvements over the previous releases, some of the main and exciting features for Spark SQL & Scala developers are AQE (Adaptive Query Execution), Dynamic Partition Pruning and other performance optimization and enhancements.

Below I’ve listed out these new features and enhancements all together in one page for better understanding and future reference.
 

1. Adaptive Query Execution (AQE)

– To process large and varying amount of data in an optimized way Spark engine makes use of its Catalyst optimizer framework. It is a Cost-Based optimizer which collects statistics from column data (like cardinality/row-count, distinct values, NULL values, min/max/avg values, etc.) and helps creating better and optimized Query Execution Plans.

– But very often at runtime due to stale Statistics query plans can go suboptimal by choosing incorrect Joins and improper partitions/reducers, thus resulting in long running queries.

– Here the AQE feature allows the optimizer to create Alternate Execution Plans at runtime which are more optimal based on the current runtime statistics of the underlying data.

–> Below are the 3 improvements in AQE:

   i. Coalesce Shuffle Partitions: it combines or coalesces adjacent small partitions into bigger partitions at runtime by looking at the shuffle file statistics, thus reducing the number of tasks to perform.

   ii. Switch Join Strategy: converts a Sort-Merge-Join to a Broadcast-Hash-Join when the optimizer finds one table size is smaller than the broadcast threshold.

   iii. Skew Join Optimization: Data Skew happens due to uneven distribution of data among partitions. Due to this in a JOIN query some partitions grow significantly bigger than the other partitions, and corresponding Tasks takes much longer time to finish than other smaller Tasks, this slows down the whole query performance. This feature reads the shuffle file statistics at runtime, detects this skew, and breaks the bigger partitions into smaller ones with the size of similar other smaller partitions, which are now optimal to be joined to the corresponding partition of the other table.

More details on: Databricks post on AQE | Spark JIRA 31412 | Baidu Case Study Video
 

2. Dynamic Partition Pruning

– In a Star schema while querying multiple Fact & Dimension tables with JOINs there are times when we apply filter on a Dimension table, but unnecessary data from the Fact tables is also scanned by the Spark query engine, resulting in slow query performance.

– This could have been avoided by Pruning such partitions at Fact tables side too, but this information is unknown to the Query engine at runtime.

– The idea is to make queries more performant by reducing the I/O operations so that only the required partitions are scanned, so developers tend to add similar filters manually at Fact tables side, this strategy is also known as Static Partition Pruning.

–> Now with the new feature of Dynamic Partition Pruning the filter at Dimension side is automatically pushed to the Fact table side (called Filter Pushdown) which Prunes more unwanted partitions, allowing Query engine to read only specific partitions and return results faster.

More details on: Databricks post & video on DPP | Spark JIRA 11150
 

3. JOIN Hints

– At times due to various reasons Spark query engine compiler is unable to make the best choice of what JOIN to choose, so developers can use appropriate JOIN hints to influence the optimizer to choose a better plan.

– In previous version of Spark i.e. 2.x only Broadcast Join hint was supported, but now with Spark 3.0 other Join hints are also supported, as follows:

   – Broadcast Hash join (BROADCAST, BROADCASTJOIN, MAPJOIN)
   – Shuffle Sort Merge join (MERGE, SHUFFLE_MERGE, MERGEJOIN)
   – Shuffle Hash join (SHUFFLE_HASH)
   – Shuffle Nested Loop join (SHUFFLE_REPLICATE_NL)

-- Spark-SQL
SELECT /*+ SHUFFLE_MERGE(Employee) */ * FROM Employee E 
INNER JOIN Department D ON E.DeptID = D.DeptID;

-- Scala
val DF_shuffleMergeJoin = 
 DF_Employee.hint("SHUFFLE_MERGE").join(DF_Department,"DeptID")

More details on: Apache Spark Docs on JOIN Hints
 

4. SQL new features and improvements

– EXPLAIN FORMATTED Header, Footer & Subqueries

– 35 new built-in Functions
   – sinh, cosh, tanh, asinh, acosh, atanh
   – any, every, some
   – bit_and, bit_or, bit_count, bit_xor
   – bool_and, bool_or
   – count_if
   – date_part
   – extract
   – forall
   – from_csv
   – make_date, make_interval, make_timestamp
   – map_entries, map_filter, map_zip_with
   – max_by, min_by
   – schema_of_csv, to_csv
   – transform_keys, transform_values
   – typeof
   – version
   – xxhash64
 

5. Other changes

– spark.sql.ansi.enabled = true: Force users to stop using the reserved keywords of ANSI SQL as identifiers.
– spark.sql.storeAssignmentPolicy = ANSI
– Compatible with Scala 2.12
– TRIM function argument order is reversed now
– Type coercion is performed per ANSI SQL standards when inserting new values into a column
 

For rest of other new features & enhancements please check Apache Spark 3.0 release notes.