Apache Kudu Overview
Apache Kudu is a columnar storage manager developed for the Hadoop platform. Kudu shares the common technical properties of Hadoop ecosystem applications: It runs on commodity hardware, is horizontally scalable, and supports highly available operation.
Apache Kudu is a top-level project in the Apache Software Foundation.
Kudu's benefits include:
- Fast processing of OLAP workloads.
- Integration with MapReduce, Spark, Flume, and other Hadoop ecosystem components.
- Tight integration with Apache Impala (incubating), making it a good, mutable alternative to using HDFS with Apache Parquet.
- Strong but flexible consistency model, allowing you to choose consistency requirements on a per-request basis, including the option for strict serialized consistency.
- Strong performance for running sequential and random workloads simultaneously.
- Easy administration and management through Cloudera Manager.
- High availability. Tablet Servers and Master use the Raft consensus algorithm, which ensures availability as long as more replicas are available than unavailable. Reads can be serviced by read-only follower tablets, even in the event of a leader tablet failure.
- Structured data model.
- Reporting applications where new data must be immediately available for end users
- Time-series applications that must support queries across large amounts of historic data while simultaneously returning granular queries about an individual entity
- Applications that use predictive models to make real-time decisions, with periodic refreshes of the predictive model based on all historical data
- CREATE/ALTER/DROP TABLE - Impala supports creating, altering, and dropping tables using Kudu as the persistence layer. The tables follow the same internal/external approach as other tables in Impala, allowing for flexible data ingestion and querying.
- INSERT - Data can be inserted into Kudu tables from Impala using the same mechanisms as any other table with HDFS or HBase persistence.
- UPDATE/DELETE - Impala supports the UPDATE and DELETE SQL commands to modify existing data in a Kudu table row-by-row or as a batch. The syntax of the SQL commands is designed to be as compatible as possible with existing solutions. In addition to simple DELETE or UPDATE commands, you can specify complex joins in the FROM clause of the query, using the same syntax as a regular SELECT statement.
- Flexible Partitioning - Similar to partitioning of tables in Hive, Kudu allows you to dynamically pre-split tables by hash or range into a predefined number of tablets, in order to distribute writes and queries evenly across your cluster. You can partition by any number of primary key columns, with any number of hashes, a list of split rows, or a combination of these. A partition scheme is required.
- Parallel Scan - To achieve the highest possible performance on modern hardware, the Kudu client used by Impala parallelizes scans across multiple tablets.
- High-efficiency queries - Where possible, Impala pushes down predicate evaluation to Kudu, so that predicates are evaluated as close as possible to the data. Query performance is comparable to Parquet in many workloads.
Example Use Cases
Streaming Input with Near Real Time Availability
A common business challenge is one where new data arrives rapidly and constantly, and the same data needs to be available in near real time for reads, scans, and updates. Kudu offers the powerful combination of fast inserts and updates with efficient columnar scans to enable real-time analytics use cases on a single storage layer.
Time-Series Application with Widely Varying Access Patterns
A time-series schema is one in which data points are organized and keyed according to the time at which they occurred. This can be useful for investigating the performance of metrics over time or attempting to predict future behavior based on past data. For instance, time-series customer data might be used both to store purchase click-stream history and to predict future purchases, or for use by a customer support representative. While these different types of analysis are occurring, inserts and mutations might also be occurring individually and in bulk, and become available immediately to read workloads. Kudu can handle all of these access patterns simultaneously in a scalable and efficient manner.
Kudu is a good fit for time-series workloads for several reasons. With Kudu's support for hash-based partitioning, combined with its native support for compound row keys, it is simple to set up a table spread across many servers without the risk of "hotspotting" that is commonly observed when range partitioning is used. Kudu's columnar storage engine is also beneficial in this context, because many time-series workloads read only a few columns, as opposed to the whole row.
In the past, you might have needed to use multiple datastores to handle different data access patterns. This practice adds complexity to your application and operations, and duplicates your data, doubling (or worse) the amount of storage required. Kudu can handle all of these access patterns natively and efficiently, without the need to off-load work to other datastores.
Data scientists often develop predictive learning models from large sets of data. The model and the data might need to be updated or modified often as the learning takes place or as the situation being modeled changes. In addition, the scientist might want to change one or more factors in the model to see what happens over time. Updating a large set of data stored in files in HDFS is resource-intensive, as each file needs to be completely rewritten. In Kudu, updates happen in near real time. The scientist can tweak the value, re-run the query, and refresh the graph in seconds or minutes, rather than hours or days. In addition, batch or incremental algorithms can be run across the data at any time, with near-real-time results.
Combining Data In Kudu With Legacy Systems
Companies generate data from multiple sources and store it in a variety of systems and formats. For instance, some of your data might be stored in Kudu, some in a traditional RDBMS, and some in files in HDFS. You can access and query all of these sources and formats using Impala, without the need to change your legacy systems.