New Snowflake features released in Q1’17
5月 09, 2017
Author: Artin Avanes
How to Use Snowflake, Snowflake Technology
We recently celebrated an important milestone in reaching 500+ customers since Snowflake became generally available in June 2015. As companies of all sizes increasingly adopt Snowflake, we wanted to look back and provide an overview of the major new Snowflake features we released during Q1 of this year, and highlight the value these features provide for our customers.
Expanding global reach and simplifying on-boarding experience
Giving our customers freedom of choice, along with a simple, secure, and guided “Getting Started” experience, was a major focus of the last quarter.
- We added a new region outside of the US; customers now have the option to analyze and store their data in Snowflake accounts deployed in EU-Frankfurt. Choosing the appropriate region is integrated into our self-service portal when new customers sign up.
- In addition, we added our high-value product editions, Enterprise and Enterprise for Sensitive Data (ESD), to our self-service offerings across all available regions. For example, with Enterprise, customers can quickly implement auto-scale mode for multi-cluster warehouses to support varying, high concurrency workloads. And customers requiring HIPAA compliance can choose ESD.
- Exploring other venues for enabling enterprises to get started quickly with Snowflake, we partnered with the AWS Marketplace team to include our on-demand Snowflake offerings, including the EU-Frankfurt option, in their newly-launched SaaS subscriptions.
Improving out-of-the-box performance & SQL coverage
We are committed to building the fastest cloud DW for your concurrent workloads with the SQL you love.
- One key performance improvement introduced this quarter was the reduction of compilation times for JSON data. Internal TPC-DS tests demonstrate a reduction between 30-60% for most of the TPC-DS queries (single stream on a single, 100TB JSON table). In parallel, we worked on improving query compile time in general, providing up to a 50% improvement in performance for short queries.
- Another new key capability is the support for bulk data inserts on a table concurrently with other DML operations (e.g. DELETE, UPDATE, MERGE). By introducing more fine-grained locking at the micro-partition level, we are able to allow concurrent DML statements on the same table.
- To improve our data clustering feature (currently in preview), we added support for specifying expressions on table columns in clustering keys. This enables more fine-grained control over the data in the columns used for clustering.
- Also, we reduced the startup time for virtual warehouses (up to XL in size) to a few seconds, ensuring almost instantaneous provisioning for most virtual warehouses.
- We extended our SQL by adding support for the ANSI SQL TABLESAMPLE clause. This is useful when a user wants to limit a query operation performed on a table to only a random subset of rows from the table.
Staying Ahead with Enterprise-ready Security
From day one, security has always been core to Snowflake’s design.
- We expanded Snowflake’s federated authentication and single sign-on capability by integrating with many of the most popular SAML 2.0-compliant identity providers. Now, in addition to Okta, Snowflake now supports ADFS/AD, Azure AD, Centrify, and OneLogin, to name just a few.
- To advance Snowflake’s built-in auditing, we introduced new Information Schema table functions (LOGIN_HISTORY and LOGIN_HISTORY_BY_USER) that users can query to retrieve the short-term history of all successful and failed login requests in the previous 7 days. If required, users can maintain a long-term history by copying the output from these functions into regular SQL tables.
Improving our ecosystem
Enabling developers and builders to create applications with their favorite tools and languages remains a high priority for us.
- With respect to enterprise-class ETL, we successfully collaborated with Talend in building a native Snowflake connector based on Talend’s new and modern connector SDK. The connector, currently in preview, has already been deployed by a number of joint customers with great initial feedback on performance and ease-of-use.
- To tighten the integration of our Snowflake service with platforms suited for machine learning and advanced data transformations, we released a new version of our Snowflake Connector for Spark, drastically improving performance by pushing more query operations, including JOINs and various aggregation functions, down to Snowflake. Our internal 10 TB TPC-DS performance benchmark tests demonstrate that running TPC-DS queries using this new v2 Spark connector is up to 70% faster compared to executing SQL in Spark with Parquet or CSV (see this Blog post for details).
- We continue to improve our drivers for our developer community. Listening to feedback from our large Python developer community, we worked on a new version of Snowflake’s native Python client driver, resulting in up to 40% performance improvements when fetching result sets from Snowflake. And, after we open-sourced our JDBC driver last quarter, we have now made the entire source code available on our official GitHub repository.
- And, last, but not least, to enhance our parallel data loading via the COPY command, ETL developers can now dynamically add file metadata information, such as the actual file name and row number, which might not be part of the initial payload.
Increasing transparency and usability
These features are designed to strike the right balance between offering a service that is easy to operate and exposing actionable insights into the running service.
- One major addition to our service is Query Profile, now general available and fully integrated into Snowflake’s web interface. Query Profile is a graphical tool you can use to detect performance bottlenecks and areas for improving query performance.
- Various UI enhancements were implemented: Snowflake’s History page now supports additional filtering by the actual SQL text and query identifier. We also added UI support for creating a Parquet file format in preparation for loading Parquet data into variant-type table columns in Snowflake.
- A new Information Schema table function (TABLE_STORAGE_METRICS) exposes information about the data storage for individual tables. In particular, a user can now better understand how tables are impacted by Continuous Data Protection, particularly Time Travel and Fail-safe retention periods, as well as which tables contain cloned data.
- We also recently introduced smarter virtual warehouse billing through Warehouse Billing Continuation (see this Blog post for details). If a warehouse is suspended and resumed within 60 minutes of the last charge, we do not charge again for the servers in the warehouse. WBC eliminates additional credit charges, and we hope it will reduce the need for our customers to strictly monitor and control when warehouses are suspended and resized.
Scaling and investing in service robustness
These service enhancements aren’t customer visible, but are crucial for scaling to meet the demands of our rapidly growing base of customers.
- As part of rolling out the new EU (Frankfurt) region, we increased automation of our internal deployment procedures to (a) further improve engineering efficiency while (b) laying the foundation for rapidly adding new regions based on customer feedback.
- We further streamlined and strengthened our various internal testing and pre-release activities, allowing us to ship new features to our customers on a weekly basis – all in a fully transparent fashion with no downtown or impact to users.
Conclusion and Acknowledgements
This summary list of features delivered in Q1 highlights the high velocity and broad range of features the Snowflake Engineering Team has successfully delivered in a short period of time. We are committed to putting our customers first and maintaining this steady pace of shipping enterprise-ready features each quarter. Stay tuned for another feature-rich Q2.
For more information, please feel free to reach out to us at firstname.lastname@example.org. We would love to help you on your journey to the cloud. And keep an eye on this blog or follow us on Twitter (@snowflakedb) to keep up with all the news and happenings here at Snowflake Computing.