Skip to main content



Copy code of Git Repo in to a different Git Repo with History Commits

 

1) Git clone 

git clone <url to Source repo> temp-dir


2) Check different branches

git branch -a

3) Checkout all the branches that you want to copy

git checkout branch-name

4) fetch all the tags 

git fetch --tags

5) Clear the link to Source repo

git remote rm origin

6) Link your local repository to your newly created NEW repository

git remote add origin <url to NEW repo>

7) Push all your branches and tags with these commands:

git push origin --all

git push --tags

8) Above steps complete copy from Source repo to New repo

Comments

Post a Comment

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark Error - missing part 0 of the schema, 2 parts are expected

 Exception -  Caused by: org.apache.spark.sql.AnalysisException : Could not read schema from the hive metastore because it is corrupted. (missing part 0 of the schema, 2 parts are expected).; Analysis -  ·          Check for table definition. In TBLProperties, you might find something like this – > spark.sql.sources.schema.numPartCols > 'spark.sql.sources.schema.numParts' 'spark.sql.sources.schema.part.0' > 'spark.sql.sources.schema.part.1' 'spark.sql.sources.schema.part.2' > 'spark.sql.sources.schema.partCol.0' > 'spark.sql.sources.schema.partCol.1' That’s what error seems to say that part1 is defined but part0 is missing.  Solution -  Drop & re-create table. If Table was partitioned  then all partitions  would have been removed. So do either of below -  ·          Msck repair table <db_name>.<table_name> ·    ...
Post a Comment
Read more




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...
Post a Comment
Read more