Google BigQuery vs Apache Druid

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Google BigQuery and Apache Druid so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Google BigQuery and Apache Druid perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Google BigQuery Overview

Google BigQuery is a fully-managed, serverless data warehouse and analytics platform developed by Google Cloud. Launched in 2011, BigQuery is designed to handle large-scale data processing and querying, enabling users to analyze massive datasets in real-time. With a focus on performance, scalability, and ease of use, BigQuery is suitable for a wide range of data analytics use cases, including business intelligence, log analysis, and machine learning.

Apache Druid Overview

Apache Druid is an open-source, real-time analytics database designed for high-performance querying and data ingestion. Originally developed by Metamarkets in 2011 and later donated to the Apache Software Foundation in 2018, Druid has gained popularity for its ability to handle large volumes of data with low latency. With a unique architecture that combines elements of time series databases, search systems, and columnar storage, Druid is particularly well-suited for use cases involving event-driven data and interactive analytics.

Google BigQuery for Time Series Data

BigQuery can be used for storing and analyzing time series data, although it is more focused on traditional data warehouse use cases. BigQuery may struggle for use cases where low latency response times are required

Apache Druid for Time Series Data

Apache Druid is designed for real time analytics and can be a good fit for working with time series data that needs to be analyzed quickly after being written. Druid also offers integrations for storing historical data in cheaper object storage so historical time series data can also be analyzed using Druid.

Google BigQuery Key Concepts

Some important concepts related to Google BigQuery include:

• Projects: A project in BigQuery represents a top-level container for resources such as datasets, tables, and views.
• Datasets: A dataset is a container for tables, views, and other data resources in BigQuery.
• Tables: Tables are the primary data storage structure in BigQuery and consist of rows and columns.
• Schema: A schema defines the structure of a table, including column names, data types, and constraints.

Apache Druid Key Concepts

• Data Ingestion: The process of importing data into Druid from various sources, such as streaming or batch data sources.
• Segments: The smallest unit of data storage in Druid, segments are immutable, partitioned, and compressed.
• Data Rollup: The process of aggregating raw data during ingestion to reduce storage requirements and improve query performance.
• Nodes: Druid’s architecture consists of different types of nodes, including Historical, Broker, Coordinator, and MiddleManager/Overlord, each with specific responsibilities.
• Indexing Service: Druid’s indexing service manages the process of ingesting data, creating segments, and publishing them to deep storage.

Google BigQuery Architecture

Google BigQuery’s architecture is built on top of Google’s distributed infrastructure and is designed for high performance and scalability. At its core, BigQuery uses a columnar storage format called Capacitor, which enables efficient data compression and fast query performance. Data is automatically partitioned and distributed across multiple storage nodes, providing high availability and fault tolerance. BigQuery’s serverless architecture automatically allocates resources for queries and data storage, eliminating the need for users to manage infrastructure or capacity planning.

Apache Druid Architecture

Apache Druid is a powerful distributed data store designed for real-time analytics on large datasets. Within its architecture, several core components play pivotal roles in ensuring its efficiency and scalability. Here is an overview of the core components that power Apache Druid.

• Historical Nodes are fundamental to Druid’s data-serving capabilities. Their primary responsibility is to serve stored data to queries. To achieve this, they load segments from deep storage, retain them in memory, and then cater to the queries on these segments. When considering deployment and management, these nodes are typically stationed on machines endowed with significant memory and CPU resources. Their scalability is evident as they can be expanded horizontally simply by incorporating more nodes.
• Broker Nodes act as the gatekeepers for incoming queries. Their main function is to channel these queries to the appropriate historical nodes or real-time nodes. Intriguingly, they are stateless, which means they can be scaled out to accommodate an increase in query concurrency.
• Coordinator Nodes have a managerial role, overseeing the data distribution across historical nodes. Their decisions on which segments to load or drop are based on specific configurable rules. In terms of deployment, a Druid setup usually requires just one active coordinator node, with a backup node on standby for failover scenarios.
• Overlord Nodes dictate the assignment of ingestion tasks, directing them to either middle manager or indexer nodes. Their deployment mirrors that of the coordinator nodes, with typically one active overlord and a backup for redundancy.
• MiddleManager and Indexer Nodes are the workhorses of data ingestion in Druid. While MiddleManagers initiate short-lived tasks for data ingestion, indexers are designed for long-lived tasks. Given their intensive operations, these nodes demand high CPU and memory resources. Their scalability is flexible, allowing horizontal expansion based on the volume of data ingestion.
• Deep Storage is a component that serves as Druid’s persistent storage unit. Druid integrates with various blob storage solutions like HDFS, S3, and Google Cloud Storage.
• Metadata Storage is the repository for crucial metadata about segments, tasks, and configurations. Druid is compatible with popular databases for this purpose, including MySQL, PostgreSQL, and Derby.