What is Apache Druid?
Apache Druid is a real-time analytics database that is designed for rapid analytics on large datasets. This database is used more often for powering use cases where real-time ingestion, high uptime, and fast query performance is needed. Druid can be used to analyze billions of rows not only in batch but also in real-time. It offers many integrations with different technologies like Apache Kafka Security, Cloud Storage, S3, Hive, HDFS, DataSketches, Redis, etc. It also follows the immutable past and append-only future. As past events happen once and never change, these are immutable, whereas the only append takes place for new events. Apache Druid provides users with a fast and deep exploration of large scale transaction data.
Characteristics of Apache Druid
Some of the exciting characteristics of Apache Druid are:
Apache Druid Use Cases
Some of the common use cases of Druid are:
- Clickstream analytics
- Server metrics storage
- OLAP/Business intelligence
- Digital Marketing/Advertising analytics
- Network Telemetry analytics
- Supply chain analytics
- Application performance metrics
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Key Features of Apache Druid
Druid’s core architecture is made by combining the ideas of different data warehouses, log search systems, and time-series databases.
1. Columnar Storage Format
It uses column-oriented storage; hence only loads required columns needed for a particular query. It helps in fast scans and aggregations.
2. Parallel Processing
It can process a query in parallel across the entire cluster. It is also termed as Massively Parallel Processing.
3. Scalable Distributed System
Druid is mostly deployed in clusters ranging from tens to hundreds that offer ingest rate of millions of records/sec, query latencies of sub-second to a few seconds, and retention of trillions of records.
4. Real-time or Batch Ingestion
Druid can ingest data either in real-time (Ingested data can be queried immediately) or in batches.
It is a fault-tolerant architecture that won’t lose data. Once Druid ingests data, its copy is safely stored in deep storage (Cloud Storage, Amazon S3, Redis, HDFS, many more). Users’ data can be easily recovered from this deep storage even if all the Druid’s servers fail. This replication ensures that queries are still possible while the system recovers.
Druid uses concise and roaring compressed bitmap indexes to create indexes that help in faster filtering.
7. Timestamp Partitioning
Every data in Druid must have a timestamp column as the data is always partitioned by time, and every query has a time filter.
8. Easy Integration with Existing Pipelines
Users can easily stream data natively using Druid from message buses like Kafka, kinesis, and many more. It can also load batch files from the data lakes like HDFS and Amazon S3.
General Architecture of Apache Druid
Druid is mainly composed of the following processes:
- Coordinator – This process manages data availability on the cluster.
- Overlord – This process controls the assignment of data ingestion workloads.
- Broker – This helps handle queries from external clients.
- Historical – This process store data that is queryable.
- Middle manager – This process is responsible for ingesting the data.
- Router – These processes are used to route requests to Brokers, Coordinators, and Overlords. These processes are optional.
The processes described above are organized into 3 types of servers: Master, Query, and Data.
It runs the Coordinator and Overlord. Basically, it manages big data ingestion and availability. Master is responsible for the ingestion of jobs and coordinating the availability of data on the “Data Servers”.
It runs Brokers and Optional Router processes. Basically, it handles queries and external clients by providing the endpoints of applications that users and clients interact with, routing queries to Data servers or other Query servers.
It runs Middle Managers and Historical processes. This helps execute jobs and store the queryable data.
Other than these 3 servers and six processes, Druid also requires storage for Metadata and Deep Storage.
It is basically used to store the metadata of the system (Audit, Datasource, Schemas, and so on). For experimental purposes, the environment suggested using Apache Derby. Derby is the default metadata store for Druid, but it is not suitable for production. For production purposes, MySQL or PostgreSQL is the best choice. Metadata storage stores the entire metadata, which is very useful for the cluster of Druid to work. Derby is not used for production as it does not support a multi-node cluster with high availability.
MySQL as a metadata storage database is used to acquire:
- Long term flexibility
- Scaling on budget
- Good with large datasets
- Good high read speed
PostgreSQL, as a metadata storage database, is used to acquire:
- Complex database designs
- Performing customized procedures
- Diverse indexing technique
- Variety of replication methods
- High read and write speed.
Apache Druid uses separate storage for any data ingested that makes it fault-tolerant. Some of Deep Storage Technologies are Cloud Storage, Amazon S3, HDFS, Redis, and many more.
Data Ingestion in Druid
Data in Druid is organized into segments that generally have rows up to a few million. Loading data in Druid is known as Ingestion or Indexing. Druid fully supports batch ingestion and streaming ingestion. Some of the technologies supported by Druid is Kinesis, Cloud Storage, Apache Kafka, and local storage. Druid requires some structure to the data it ingests. In general, data should consist of OS timestamp, metrics and dimensions.
Zookeeper for Apache Druid
Apache Druid uses Apache Zookeeper to integrate all the services. Users can use Zookeeper that comes with Druid for experiments, but one has to install Zookeeper for production. Apache Druid’s cluster can only be as stable by a Zookeeper. Zookeeper is responsible for most of the communications that keep the Druid cluster functioning as Druid nodes are prevented from talking to each other.
Duties of a Zookeeper
Zookeeper is responsible for the following operations:
- Segment “publishing” protocol from Historical
- Coordinator leader election
- Overlord and MiddleManager task management
- Segment load/drop protocol between Coordinator and Historical
- Overlord leader election
|Don’t forget to explore our blog on Apache Zookeeper|
How to Keep a Zookeeper Stable?
For maximum Zookeeper stability, the user has to follow the following practices:
- There should be a Zookeeper dedicated to Druid; avoid sharing it with any other products/applications.
- Maintain an odd number of Zookeepers for increased reliability.
- For highly available Zookeeper, 3-5 Zookeeper nodes are recommended. Users can either install Zookeeper on their own system or run 3 or 5 master servers and configure Zookeeper on them appropriately.
- Share Zookeeper’s location with a master server rather than doing so with data or query servers. This is done because query and data are far much work-intensive than the master node (coordinator and overlord).
- To fully achieve high availability, it is recommended to never out Zookeeper behind a load balancer.
If Zookeeper goes down, the cluster will operate. Failing of Zookeeper would neither result in addition to new data segments nor can it effectively react to the loss of one of the nodes. So, the failure of Zookeeper is a degraded state.
Users can monitor Druid by using the metrics it generates. Druid generates metrics related to queries, coordination and ingestion. These metrics are emitted as a JSON object. It is either emitted to a runtime log file or over HTTP (to service like Kafka). The emission of a metric is disabled by default.
Fields of Metrics Emitted
Metrics emitted by Druid share a common set of fields.
- Timestamp – the time at which metric was created
- Metric – the name given to the metric
- Service – the name of the service that emitted the metric
- Host – the name of the host that emitted the metric
- Value – the numeric value that is associated with the metric emitted
Briefing About Available Metrics
Metric emitted may have dimensions beyond the one listed. To change the emission period of Druid that is 1 minute by default, one can use `druid.monitoring.emissionPeriod` to change the default value. Metrics available are:
- Query Metrics, mainly categorized as Broker, Historical, Real-time, Jetty and Cache
- SQL Metrics (Only if SQL is enabled)
- Ingestion Metrics (Kafka Indexing Service)
- Real-time Metrics (Real-time process, available if Real-time Metrics Monitor is included)
- Indexing Service
- JVM (Available if JVM Monitor module is included)
- Event Receiver Firehose (available if Event Receiver Firehose Monitor module is included)
- Sys (Available if Sys Monitor module is included)
- General Health, mainly Historical
Apache Druid is the best in the market when it comes to analyzing data in clusters and providing brief insight to all the data processed. Plus having Zookeeper by the side, one can ease up their working with Apache Druid and rule the DataOps market. Also, there are many libraries to interact with Apache Druid. To Validate the running of services, one can use JPS commands. As Druid nodes are java processes, they would show up when JPS commands ‘$ jps -m’ are used. With that much ease in monitoring Druid and working with such a vast architecture of Druid, it is really the last bite of an ice-cream for a DataOps Engineer.