Andrew Kramer

3月 292018
 

With the release of SAS Viya 3.3, you now have the ability to pass implicit SQL queries to a variety of SQL data sources, including Hive. Under an implicit pass-through, users can write SAS compliant SQL code, and SAS will:

  1. Convert as much code as possible into database native SQL.
  2. Execute the resulting query in-database.
  3. Bring the result back into SAS Viya.

My SAS Viya is co-located within a Hortonworks Hadoop environment. Within this environment, I have set up multiple tables within Hive, which provides structure and a query-like environment for Hadoop data. Using the SAS Data Explorer in SAS Viya, I can easily see the different tables in the Hive environment, and visually inspect them without having to load the data into SAS. The screenshot below shows the Hive table va_service_detail, which contains anonymous data related to recent hospital stays.

SQL Pass-through to Hive in SAS Viya

In my Hive environment, I have a second table called va_member_detail, which contains information about the individuals who were hospitalized in the above table, va_service_detail. A summary of this Hive table can be found in the screenshot below.

Using this data, I would like to perform an analysis to determine why patients are readmitted to the hospital, and understand how we can preventatively keep patients healthy. I will need to join these two tables to allow me to have visit-level and patient-level information in one table. Since medical data is large and messy, I would like to only import the needed information into SAS for my analysis.  The simplest way to do this is through an implicit SQL pass-through to Hive, as shown below:

With an implicit pass-through, I write normal SAS FedSQL code on top of a SAS Library called “Hadoop” pointing to my Hive Server. Once the code is submitted, the SAS System performs the following steps:

  1. Translates the SAS FedSQL code into HiveQL.
  2. Executes the HiveQL script in Hive.
  3. Loads the resulting data in parallel into SAS.

Looking at the log, we can see that the SQL statement was “Fully offloaded to the underlying data source via fill pass-through”, meaning that SAS successfully executed the query, in its entirety, in Hive. With the SAS Embedded Process for Hadoop, the resulting table is then lifted in-parallel from Hive into SAS Viya, making it available for analysis.

As we can see in the log, it took 42 seconds to execute the query in Hive, and bring the result into SAS. To compare efficiency, I redid the analysis, loading va_service_detail and va_member_detail into the memory of the SAS server and performed the join there. The execution took 58 seconds, but required three in-memory tables to do so, along with much more data passing through the network. The implicit pass-through has the benefits of increased speed and decreased latency in data transfer by pushing the query to its source, in this case Hive.

Conclusion

The Implicit SQL Pass-through to Hive in SAS Viya is a must have tool for any analyst working with Hadoop data. With normal SQL syntax in a familiar SAS interface, analysts can push down powerful queries into Hive, speeding up their analysis while limiting data transfer. Analysts can effectively work with large ever-growing data sizes, and speed up the time to value on solving key business challenges.

Implicit SQL Pass-through to Hive in SAS Viya was published on SAS Users.

1月 182018
 

One of the most exciting features from the newest release of Visual Data Mining and Machine Learning on SAS Viya is the ability to perform Market Basket Analysis on large amounts of transactional data. Market Basket Analysis allows companies to analyze large transactional files to identify significant relationships between items. While most commonly used by retailers, this technique can be used by any company that has transactional data.

For this example, we will be looking at customer supermarket purchases over the past month. Customer is the Transaction ID; Time is the time of purchase; and Product is the item purchased. The data must be transactional in nature and not aggregated, with one row for each product purchased by each customer.

Market Basket Analysis in SAS Viya

With our data ready, we can now perform the analysis using the MBANALYSIS Procedure. As illustrated below in SAS Studio, by specifying pctsupport=1, we will only look at items, or groups of items, that appear in at least 1% of the transactions. For very large datasets this saves time by only looking at combinations of items that appear frequently. This allows extraction of the most common and most useful relationships.

The MBANALYSIS procedure outputs a list of significant relationships, called Association Rules, by calculating the LIFT metric. A lift greater than one generally indicates that a Rule is significant. By default, each relationship has two items, although this can be changed to include multiple items.

Below is a screenshot of the ten most important rules. The first item in the rule is the “Left Hand Side” and the second item after the arrow is the “Right Hand Side.” For the first rule, we can see that coke and ice cream appear together in 220 transactions and have a lift of 2.37, meaning purchasing Coke makes the purchase of ice cream about twice as likely.

Top 10 Association Rules

While Association Rules above give powerful insights into large transactional datasets, the challenge is exploring these rules visually. One way to do this is by linking the rules together via a Network Diagram. This allows users to see the relationships between multiple rules, and identify the most important items in the network. The following SQL code prepares the data for the Network Diagram.

Network Diagrams plot a set of “Source” values (T1_ITEM), and connects them to a “Target” value (ITEM2). If the source value represents the left hand side of the rule, the corresponding right hand side of the rule is listed as the Target variable. We will use the “Lift” value to link these source and target variables. If the target value is the right hand side of the rule, the target and the lift are missing. This allows us to plot the product, but no linkage will be made.

Now, my data is ready to be visualized as a Network Diagram. Using the following code, I am able to promote my Association Rules, making this dataset available via SAS® Visual Analytics.

Now, I am able to quickly and easily generate my Network Diagram without having to create any code.

Hovering over a node allows me to see specific information about that particular item. Here, we can see that Heineken was purchased in 59.9% of all transactions, which is 600 transactions.

Hovering over the linkage, we can see specific information about the rule. Below, we can see that purchasing artichoke (artichoke) makes the purchase of Heineken about 38% more likely. Many other rules link to Heineken, showing its importance in the network. Business Unit Experts can use this diagram as a starting point to analyze selling strategies to make proper adjustments for the business.

Conclusion

The Market Basket Analysis procedure in Visual Data Mining and Machine Learning on SAS Viya can help retailers quickly scan large transactional files and identify key relationships. These relationships can then be visualized in a Network Diagram to quickly and easily find important relationships in the network, not just a set of rules. As transactional data, whether in-store, online, or in any other form gets bigger, this Market Basket functionality is a must have weapon in the analytical toolkit of any business.

Visualizing the results of a Market Basket Analysis in SAS Viya was published on SAS Users.