New Uses for Directed Queries

Directed queries were introduced in Vertica 7.2. Directed queries were originally designed to achieve two goals: • Preserve current query plans before a scheduled upgrade. • Enable you to create query plans that improve optimizer performance. Since their introduction, users have found new and compelling ways to use directed queries—notably, using them to substitute one...

What’s New in Vertica 8.1: Flex Tables Enhancements

This blog post was authored by Soniya Shah. As of Vertica 8.1, you can execute CTAS statements to create flex tables. CREATE TABLE AS (CTAS) statement Previously, Vertica supported creating tables using the AS SELECT clause. Frequently called CTAS, this SQL statement lets you create a new table that contains the results from querying another...

Use MERGE to Update 1 Million Rows in 2 Seconds

This blog post was co-authored by Yassine Faihe, Michael Flower, and Moshe Goldberg. Updating One Million Records in Two Seconds To illustrate the true power of MERGE, this article describes how we used MERGE to demonstrate Vertica's performance at scale. SQL MERGE statements combine INSERT and UPDATE operations. They are a great way to update...

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Query Optimization Using Projections

In Vertica, tables are logical representations of the data. Vertica stores the actual data in projections. When data is loaded into a Vertica table, Vertica creates or updates a column-store projection. Vertica also compresses and/or encodes projection data, optimizing data access and storage. If you experience performance issues, your best first step is to run...

Machine Learning Mondays: How Vertica Implements Efficient and Scalable Machine Learning

This blog post was authored by Vincent Xu. As of Vertica 8.1, Vertica has introduced a set of popular machine learning algorithms, including Linear Regression, Logistic Regression, Kmeans, Naïve Bayes, and SVM. Based on our recent benchmarks, they run faster than MLlib on Apache Spark. The following chart shows the performance difference between Vertica 8.1.0...
Modern Database Analytics

Big Flat Fact Tables

This blog post was authored by Steve Sarsfield. For decades, it's been widely accepted that snowflake and star schemas facilitate getting optimal performance from your data warehouse. You normalize data by identifying the rows of data that you typically ingest, and creating a schema that is optimized for the types of queries you want to...

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Using Vertica and HyperLogLog

This is a guest blog post co-authored by Francois Jehl and Pawel Szostek. Francois is the lead of the Analytics Data Storage team at Criteo; Pawel is a software engineer in the Analytics Data Storage team at Criteo. Criteo is the global leader in digital performance advertising with 900B ads served in 2016. The R&D...

Machine Learning Mondays: Data Preparation for Machine Learning in Vertica

This blog post was authored by Vincent Xu. This post is part of our Machine Learning Mondays series. Stay tuned for more! Introduction Machine learning (ML) is an iterative process. From understanding data, preparing data, building models, testing models to deploying models, every step of the way requires careful examination and manipulation of the data....

Using Hadoop Rack Locality to Boost Vertica Performance

This blog post was authored by Monica Cellio. When database nodes are co-located on Hadoop data nodes, Vertica can take advantage of the Hadoop rack configuration to execute queries against ORC and Parquet data. Moving query execution closer to the data reduces network latency and can improve performance. Vertica automatically uses database nodes that are...

What’s New in Vertica 8.1: Connecting to Vertica Updates

Vertica 8.1 includes the following product enhancements to Connecting to Vertica. Functional Updates to \timing The \timing metafunction has been enhanced so you can use the following commands to toggle \timing on or off based on its current setting: •\timing – turns timing on or off depending on its current state. For example if timing...