Skew Analytics

[Sherry Galeazzi]

Hash-distribution improves query performance on large fact tables, and is the focus of this article. Round-robin distribution is useful for improving loading speed. These design choices have a significant effect on improving query and loading performance.

Another table storage option is to replicate a small table across all the Compute nodes. For more information, see Design guidance for replicated tables. To quickly choose among the three options, see Distributed tables in the tables overview.

Since identical values always hash to the same distribution, SQL Analytics has built-in knowledge of the row locations. In dedicated SQL pool this knowledge is used to minimize data movement during queries, which improves query performance.

Hash-distributed tables work well for large fact tables in a star schema. They can have very large numbers of rows and still achieve high performance. There are some design considerations that help you to get the performance the distributed system is designed to provide. Choosing a good distribution column or columns is one such consideration that is described in this article.

A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random. Unlike hash-distributed tables, rows with equal values are not guaranteed to be assigned to the same distribution.

As a result, the system sometimes needs to invoke a data movement operation to better organize your data before it can resolve a query. This extra step can slow down your queries. For example, joining a round-robin table usually requires reshuffling the rows, which is a performance hit.

Hash distribution can be applied on multiple columns for a more even distribution of the base table. Multi-column distribution allows you to choose up to eight columns for distribution. This not only reduces the data skew over time but also improves query performance. For example:

Multi-column distribution in Azure Synapse Analytics can be enabled by changing the database's compatibility level to 50 with this command.ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = 50;For more information on setting the database compatibility level, see ALTER DATABASE SCOPED CONFIGURATION. For more information on multi-column distributions, see CREATE MATERIALIZED VIEW, CREATE TABLE, or CREATE TABLE AS SELECT.

Choosing distribution columns is an important design decision since the values in the hash columns determine how the rows are distributed. The best choice depends on several factors, and usually involves tradeoffs. Once a distribution column or column set is chosen, you cannot change it. If you didn't choose the best columns the first time, you can use CREATE TABLE AS SELECT (CTAS) to re-create the table with the desired distribution hash key.

For best performance, all of the distributions should have approximately the same number of rows. When one or more distributions have a disproportionate number of rows, some distributions finish their portion of a parallel query before others. Since the query can't complete until all distributions have finished processing, each query is only as fast as the slowest distribution.

To get the correct query result queries might move data from one Compute node to another. Data movement commonly happens when queries have joins and aggregations on distributed tables. Choosing a distribution column or column set that helps minimize data movement is one of the most important strategies for optimizing performance of your dedicated SQL pool.

After data is loaded into a hash-distributed table, check to see how evenly the rows are distributed across the 60 distributions. The rows per distribution can vary up to 10% without a noticeable impact on performance.

A quick way to check for data skew is to use DBCC PDW_SHOWSPACEUSED. The following SQL code returns the number of table rows that are stored in each of the 60 distributions. For balanced performance, the rows in your distributed table should be spread evenly across all the distributions.

A good distribution column set enables joins and aggregations to have minimal data movement. This affects the way joins should be written. To get minimal data movement for a join on two hash-distributed tables, one of the join columns needs to be in distribution column or columns. When two hash-distributed tables join on a distribution column of the same data type, the join does not require data movement. Joins can use additional columns without incurring data movement.

It is not necessary to resolve all cases of data skew. Distributing data is a matter of finding the right balance between minimizing data skew and data movement. It is not always possible to minimize both data skew and data movement. Sometimes the benefit of having the minimal data movement might outweigh the effect of having data skew.

To decide if you should resolve data skew in a table, you should understand as much as possible about the data volumes and queries in your workload. You can use the steps in the Query monitoring article to monitor the effect of skew on query performance. Specifically, look for how long it takes large queries to complete on individual distributions.

A Flink application is executed on a cluster in a distributed fashion. To scale out to multiple nodes, Flink uses the concept of keyed streams, which essentially means that the events of a stream are partitioned according to a specific key, e.g., customer id, and Flink can then process different partitions on different nodes. Many of the Flink operators are then evaluated based on these partitions, e.g., Keyed Windows, Process Functions and Async I/O.

You can identify skew in the partitions by comparing the records received/sent of subtasks (i.e., instances of the same operator) in the Flink dashboard. In addition, Managed Service for Apache Flink monitoring can be configured to expose metrics for numRecordsIn/Out and numRecordsInPerSecond/OutPerSecond on a subtask level as well.

While we live in a world where every business decision is based on data, we invest millions in marketing and business intelligence software, analytical tools, and data experts. As marketers and analysts "in the dark," our data assumes that all unique visitors are real humans. Thus, are we making decisions based on bots and fake users skewing crucial business metrics?

When marketers and analysts are "in the dark," their data assumes that all unique visitors are real humans, our data shows that 22.3% are bots on average. Unique site visits are defined as a session on a website originating from a single user or source. This data shows that 77.7% of unique site visits actually come from real human users on average.

The concept of skewness is baked into our way of thinking. When we look at a visualization, our minds intuitively discern the pattern in that chart, whether we are data scientists or beginners working on a python dataset.

Now, we know that skewness of data is the measure of the lack of symmetry, and its types are distinguished by the side on which the tail of probability distribution lies. But why is knowing the skewness of the data important?

First, linear models work on the assumption that the distribution of the independent variable and the target variable are similar. Therefore, knowing about the skewness in statistics of data helps us create better linear models.

Since our data is positively skewed here, it means that it has a higher number of data points having low values, i.e., cars with less horsepower. So when we train our model on this data, it will perform better at predicting the mpg of cars with lower horsepower as compared to those with higher horsepower.

The central limit theorem says that the sampling distribution of the mean will always be normally distributed as long as extreme values or the sample size is large enough. Regardless of whether the population has a normal, Poisson, binomial, or any other distribution, the sampling distribution of the mean will be normal.

Hypothesis Testing uses the Central Limit Theorem. Using the Central Limit Theorem, we can extend the approach employed in Single Sample Hypothesis Testing for normally distributed populations to those that are not normally distributed.

A positive skewness is the right-skewed distribution with the long tail on its right side. The value of skewness of data for a positively skewed distribution is greater than zero. As you might have already understood by looking at the figure, the value of the mean is the greatest one, followed by the median and then by mode.

Well, the answer to that is that the skewness of the data distribution is on the right; it causes the mean to be greater than the median and eventually move to the right. Also, the mode occurs at the highest frequency of the distribution, which is on the left side of the median. Therefore, the measure of central tendencies is mode < median < mean.

Here, Q2-Q1 and Q3-Q2 are equal, and yet the distribution is positively skewed. The keen-eyed among you will have noticed the length of the right whisker is greater than the left whisker. From this, we can conclude that the data is positively skewed.

As you might have already guessed, a negatively skewed distribution is the left-skewed distribution with the long tail on its left side. The value of skewness for a negatively skewed distribution is less than zero. You can also see in the above figure that the measure of central tendencies is mean < median < mode.

Similar to what we did earlier, if Q3-Q2 and Q2-Q1 are equal, then we look for the length of whiskers. And if the length of the left whisker is greater than that of the right whisker, then we can say that the data is negatively skewed.

Skewness is a measure of lack of symmetry. It is a shape parameter that characterizes the degree of asymmetry of a distribution. A distribution is said to be positively skewed with a degree of skewness greater than 0 when the tail of a distribution is toward the high values indicating an excess of low values. Conversely, it is negatively skewed with a degree of skewness less than 0 (Sk