smalltable | An on-disk key-value store with very few features | Key Value Database library

When you declare an int array as you did here:

your problem of query being slow has everything to do with indexing. if you have 20000 record in smallTable and 2,000,000 records in largeTable1 without indexing it will scan 40billion rows to give you result but if you index the large table it will be reduced to 200,000 and if you index smallTable as well it will scan only 10,000 rows. So indexing all ID fields is the most critical part of your problem.

You could use a subquery for amx abs(

Under the assumption that do_join shall be called outside the loop, with all tables patched, you could write a fixture that uses contextlib.ExitStack to setup all mocks:

Look at @@ROWCOUNT

This returns the number of rows affected by the last procedure.

Using your data and the expected results please consider the following.

I would also mention that you have a 0 for New CategoryID of 842, but following the rules you listed it would be a value of 62. Not sure how you came to get 0 for this.

These parameters are used to make decision on when to use Map Join against Common join in hive, which ultimately affects query performance at the end.

Map join is used when one of the join tables is small enough to fit in the memory, so it is very fast. here's the explanation of all parameters:

hive.auto.convert.join

When this parameter set to true, Hive will automatically check if the smaller table file size is bigger than the value specified by hive.mapjoin.smalltable.filesize, if it's larger than this value then query execute through common join. Once auto convert join is enabled, there is no need to provide the map join hints in the query.

hive.auto.convert.join.noconditionaltask

When three or more tables are involved in join, and

hive.auto.convert.join = true - Hive generates three or more map-side joins with an assumption that all tables are of smaller size.

hive.auto.convert.join.noconditionaltask = true, hive will combine three or more map-side joins into a single map-side join if size of n-1 table is less than 10 MB. Here size is defined by hive.auto.convert.join.noconditionaltask.size.

hive.mapjoin.smalltable.filesize

This setting basically the way to tell optimizer the definition of small table in your system. This value defines what is small table for you and then when query executes based on this value it determines if join is eligible to convert into map join.

hive.auto.convert.join.noconditionaltask.size

The size configuration enables the user to control what size table can fit in memory. This value represents the sum of the sizes of tables that can be converted to hashmaps that fit in memory.

Here's the very good explanation link which includes description for all 4 parameters with an example:

http://www.openkb.info/2016/01/difference-between-hivemapjoinsmalltabl.html

So the INNER JOIN syntax you are using I believe is incorrect. After the INNER JOIN table that will be joined, you need to state ON what columns you wish to join the tables on.

The following query is the correct syntax (although it may not be correct for your implementation).

There are many things wrong here, but a handful include:

• Running this operation 1000 times and taking the difference of nanoTime for each of them does not make your benchmark valid. Seriously, use JMH. Or at least run it, like, ten million times.
• Your hash table doesn't actually work any differently for different-sized tables. You use table[getHash(key) % RADIX], which basically means however big the table is, you only use 10 buckets in it and pretend the rest don't exist.
• System.identityHashCode is not a useful hash function, especially on strings, especially when you're hoping to actually find an element that's in there...or not.
• While you're at it, you're not using Node.next as a field, and might as well get rid of it.