How do you handle data skewness?

Dealing with skew data:

1. log transformation: transform skewed distribution to a normal distribution.
2. Remove outliers.
3. Normalize (min-max)
4. Cube root: when values are too large.
5. Square root: applied only to positive values.
6. Reciprocal.
7. Square: apply on left skew.

How will you optimize when joining two large tables spark?

3 Answers

1. Use a broadcast join if you can (see this notebook).
2. Consider using a very large cluster (it’s cheaper that you may think).
3. Use the same partitioner.
4. If the data is huge and/or your clusters cannot grow such that even (3) above leads to OOM, use a two-pass approach.

What is data skewness in spark?

Usually, in Apache Spark, data skewness is caused by transformations that change data partitioning like join, groupBy, and orderBy. For example, joining on a key that is not evenly distributed across the cluster, causing some partitions to be very large and not allowing Spark to process data in parallel.

How does spark determine data skew?

Identifying and resolving data skew. Spark users often observe all tasks finish within a reasonable amount of time, only to have one task take forever. In all likelihood, this is an indication that your dataset is skewed. This behavior also results in the overall underutilization of the cluster.

How much skew is too much?

The rule of thumb seems to be: If the skewness is between -0.5 and 0.5, the data are fairly symmetrical. If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed. If the skewness is less than -1 or greater than 1, the data are highly skewed.

How can I improve my spark join performance?

To accomplish ideal performance in Sort Merge Join: Make sure the partitions have been co-located. Otherwise, there will be shuffle operations to co-locate the data as it has a pre-requirement that all rows having the same value for the join key should be stored in the same partition.

How do you handle skew join in spark?

And currently, there are mainly 3 approaches to handle skew join:

1. Increase the parallelism number of “spark.
2. Increase the broadcast hash join threshold to change the sort-merge join to broadcast hash join as far as possible and then eliminate the skew join case brought by shuffle;

How do you handle skewness in hive?

Using Hive Configuration Whether to enable skew join optimization. The algorithm is as follows: At runtime, detect the keys with a large skew. Instead of processing those keys, store them temporarily in an HDFS directory. In a follow-up map-reduce job, process those skewed keys.

What is data skew?

Data skew primarily refers to a non uniform distribution in a dataset. The direct impact of data skew on parallel execution of complex database queries is a poor load balancing leading to high response time.

What is good skewness and kurtosis?

The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). Hair et al. (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.

How do you report skewness in statistics?

As a general rule of thumb:

1. If skewness is less than -1 or greater than 1, the distribution is highly skewed.
2. If skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed.
3. If skewness is between -0.5 and 0.5, the distribution is approximately symmetric.