We are thrilled that Gartner has positioned Google as a Leader for the second year in a row in the 2021 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS).

We believe the report evaluated Google’s unified capabilities across both transactional and analytical use cases, and showcases innovation progress in areas like data management consistency, high speed processing and ingestion, security, elasticity, advanced analytics, and more. 

With the recent announcement of Dataplex, organizations can centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls.  Solutions like BigQuery ML provide a “built-in” approach for advanced analytics capabilities and Analytics Hub offer the infrastructure customers need to share data analytics solutions securely and at scale in ways never before achieved. For example, over a seven-day period in April, more than 3,000 different organizations shared over 200 petabytes of data using BigQuery. 

Research shows 90% of organizations have a multicloud strategy, which is why we have invested in a cross-cloud data analytics solution for Google Cloud, AWS, and Azure with BigQuery Omni. Additionally, our progress with Anthos and our Distributed Cloud this past year further advance our ability to support multi and hybrid cloud scenarios. To gain a competitive advantage using data, organizations need a data platform that transcends transactional and analytical workloads and can be run with the highest level of reliability, availability and security. Cloud Spanner, our globally distributed relational database has redefined the scale, global consistency, and availability of Online Transaction Processing (OLTP) systems. Spanner processes over 1 billion requests per second at peak, and has been battle-tested with some of the most-demanding applications, including Google services such as Search, YouTube, Gmail, Maps, and Payments. And what’s unique about our core services Spanner and BigQuery, is that they leverage common infrastructure such as our highly durable distributed file system (Colossus), our large-scale cluster management system (Borg), and Jupiter, our high-performance networking infrastructure, enabling features such as federation between Spanner and BigQuery.

We remain focused on the integration of Google Trends, Maps, Search, Ads, and have increased industry domain expertise in areas such as retail, financial services, healthcare, and gaming. We’re continuing to develop industry white papers such as this one – How to develop Global Multiplayer Games using Cloud Spanner – and we’re proud of the work the team has done to create and share industry and horizontal architecture patterns built from industry leaders to serve as solution accelerators for customer use cases. 

Innovation momentum continues with a unified and open data cloud

We continue to innovate across our data cloud portfolio, especially with the innovations we announced at Google Cloud NEXT’21. BigQuery Omni is now available for AWS and Azure, supporting cross-cloud analytics for customers. We’ve added additional capabilities for enterprise data management and governance with Dataplex, which recently went GA. We’ve made migrations to Cloud SQL easier and faster with the Database Migration Service. More than 85% of all migrations are underway in under an hour, with the majority of customers migrating their databases from other clouds. We are embracing openness with Spanner by adding a PostgreSQL interface, allowing enterprises to take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem. We are also automating data processing with Spark on Google Cloud, which enables developers to spend less time on infrastructure management and more time on data science, modeling, and delivering business value. Finally, we announced Google Earth Engine on Google Cloud, allowing customers to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and the Google Maps Platform. 

With these innovations, enterprises like PayPal, Deutsche Bank, and Equifax use Google Cloud to solve for their end-to-end data lifecycle use cases. Organizations like Telefonica use Google Cloud to deliver new customer experiences. Telefónica has transformed every aspect of how they store, share, and analyze data while doubling processing power and lowering costs.

We continue to support an open ecosystem of data partners including Informatica, Tableau, MongoDB, Neo4j, C3.ai, and Databricks, giving customers the flexibility of choice to build their data clouds without being locked into a single approach. 

We are honored to be a Leader in the 2021 Gartner Magic Quadrant for Cloud Database Management Systems (DBMS), and look forward to continuing to innovate and partner with you on your digital transformation journey. 

Download the complimentary 2021 Gartner Magic Quadrant for Cloud Database Management Systems report.

Learn more about how organizations are building their data clouds with Google Cloud solutions. 

^{Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Merv Adrian, Rick Greenwald, Adam Ronthal, Philip Russom, 14 December 2021.}

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Today we’re announcing the HarbourBridge Schema Assistant, which provides a guided schema-design workflow for migrating from MySQL or PostgreSQL to Spanner. HarbourBridge imports dump files (from mysqldump or pg_dump) or directly connects to your source database, and converts the source database schema to an equivalent Spanner schema. The new Schema Assistant capability displays the source schema and Spanner schema side-by-side, highlights errors and walks you through a series of steps to validate and optimize your Spanner schema. It also produces a browsable assessment report with an overall migration-fitness score for Spanner, a table-by-table detailed analysis of type mappings and a list of features used in the source database that aren’t supported by Spanner. It supports editing of table and column names, column types, primary keys and constraints, as well as dropping of tables, columns, foreign keys and secondary indexes.

The new Schema Assistant complements HarbourBridge’s existing data and schema migration capabilities and is a critical step towards our goal of building a complete open-source migration toolkit. HarbourBridge continues to support command-line schema and data migration and turn-key Spanner evaluation.

Complementing the bulk data migration capabilities of HarbourBridge, we are also announcing the ability to migrate change events from MySQL to Cloud Spanner.

Supported Features in Schema Assistant

1. Global type mapping. Users can customize the global mapping for how types should be mapped to Spanner consistently across the schema. For example, mapping large integers in source schema to Spanner’s NUMERIC.
2. Local type mapping. Users can override the custom type mapping for a given table/column.
3. Session management. A session keeps track of all the changes made to the schema mapping.
4. Customization of secondary indexes. Users can add, edit and delete secondary indexes to optimize their Spanner performance.
5. Customization of foreign keys and interleaved tables. Table interleaving is an important design consideration when migrating to Cloud Spanner as explained in more detail in this blog post.

Features in the pipeline

We are already working to further expand the supported set of schema editing features and welcome your feedback. We are particularly excited to expand the Schema Assistant’s design recommendations for optimizing Spanner schemas e.g. in-depth recommendations for primary key design.

HarbourBridge is open source and we gladly accept contributions from the wider community.

BigQuery offers strong query performance, but it is also a complex distributed system with many internal and external factors that can affect query speed. When your queries are running slower than expected or are slower than prior runs, understanding what happened can be a challenge.

The query execution graph provides an intuitive interface for inspecting query execution details. By using it, you can review the query plan information in graphical format for any query, whether running or completed.

You can also use the query execution graph to get performance insights for queries. Performance insights provide best-effort suggestions to help you improve query performance. Since query performance is multi-faceted, performance insights might only provide a partial picture of the overall query performance.

Execution graph

When BigQuery executes a query job, it converts the declarative SQL statement into a graph of execution, broken up into a series of query stages, which themselves are composed of more granular sets of execution steps. The query execution graph provides a visual representation of the execution stages and shows the corresponding metrics. Not all stages are made equal. Some are more expensive and time consuming than others. The execution graph provides toggles for highlighting critical stages, which makes it easier to spot the potential performance bottlenecks in the query.

Query performance insights

In addition to the detailed execution graph BigQuery also provides specific insights on possible factors that might be slowing query performance.

Slot contention

When you run a query, BigQuery attempts to break up the work needed by your query into tasks. A task is a single slice of data that is input into and output from a stage. A single slot picks up a task and executes that slice of data for the stage. Ideally, BigQuery slots execute tasks in parallel to achieve high performance. Slot contention occurs when your query has many tasks ready for slots to start executing, but BigQuery can’t get enough available slots to execute them.

Insufficient shuffle quota

Before running your query, BigQuery breaks up your query’s logic into stages. BigQuery slots execute the tasks for each stage. When a slot completes the execution of a stage’s tasks, it stores the intermediate results in shuffle. Subsequent stages in your query read data from shuffle to continue your query’s execution. Insufficient shuffle quota occurs when you have more data that needs to get written to shuffle than you have shuffle capacity.

Data input scale change

Getting this performance insight indicates that your query is reading at least 50% more data for a given input table than the last time you ran the query and hence experiencing query slowness. You can use table change history to see if the size of any of the tables used in the query has recently increased.

What’s next?

We continue to work on improving the visualization of the graph. We are working on adding additional metrics to each step and adding more performance insights that will make query diagnosis significantly easier. We are just getting started.