Non-Equi Left Join with Hive & Spark

A non-equi left join is a join between 2 tables in which join condition is given by operators other than equal to "=".

For example -

Equi Join - "Table1 join Table2 on (T1.c1=T2.c1)"
Non-Equi Join - "Table1 join Table2 on (T2.c1 > T1.c1 and T2.c1<T1.c2)"

Hive does not support Non-Equi join.

Spark supports Non-Equi join. But, we have seen that it only works when there is a combination of Equi & Non-Equi columns. For example -

Table1 join Table2 on (T1.c3=T2.c3 and T2.c1 > T1.c1 and T2.c1<T1.c2)

But, if you just have Non-Equi condition then Spark job fails after sometime with "Future Timeout" exception
Table1 join Table2 on (T2.c1 > T1.c1 and T2.c1<T1.c2)

In that scenario, we have to identify additional column on which Equi condition can be specified . It can be a hash value column or something else that matches in Table 1 and Table 2.

For example - We had 2 tables and wanted to check if IP is between start range and end range. On analysis, we found that first 2 colon separated values can be compared. So, we modified our join condition like below and it worked -

regexp_extract(B.CLIENT_IPV6_ADDRESS, '[0-9a-fA-F]+:[0-9a-fA-F]+:', 0) = regexp_extract(C.ip_address, '[0-9a-fA-F]+:[0-9a-fA-F]+:', 0) and C.ip_range_start <= B.CLIENT_IPV6_ADDRESS and C.ip_range_end >= B.CLIENT_IPV6_ADDRESS

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

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Say we have a JSON String like below - { "billingCountry":"US" "orderItems":[ { "itemId":1, "product":"D1" }, { "itemId":2, "product":"D2" } ] } And, our aim is to get output parsed like below - itemId product 1 D1 2 D2 First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),&

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Exception - Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet

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One may face following error while copying data from one cluster to other, using Distcp Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path {src} in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use {src} as "/a/*/*" then you will get the error "File would cause duplicates." But, if you use {src} as "/a" then you will not get error in copying.

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