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Apache Hive vs Splunk: What are the differences?
Key Differences between Apache Hive and Splunk
Apache Hive and Splunk are two popular tools used for data processing and analysis, but they have several key differences.
Data Source and Structure: Apache Hive is primarily designed to work with structured data stored in Apache Hadoop. It uses a SQL-like query language called HiveQL to process and analyze data. On the other hand, Splunk is a log management and analysis platform that can work with various data sources, including logs, metrics, and machine-generated data.
Querying Capability: Apache Hive is optimized for batch processing and is suitable for running complex, long-running queries on large datasets. It supports advanced SQL-like queries and provides a high-level abstraction for data processing. Splunk, on the other hand, is designed for real-time searching and analysis. It allows you to perform ad-hoc searches and visualizations on live data streams.
User Interface: Apache Hive primarily relies on a command-line interface and a web-based interface called Hive Web UI for interacting with the system. It provides a user-friendly environment for managing and executing queries. Splunk, on the other hand, offers a comprehensive and intuitive web-based interface that allows users to search, analyze, and visualize data easily.
Scalability and Performance: Apache Hive is built on top of Apache Hadoop and leverages its distributed processing capabilities for scalability. It can handle large datasets and parallelize query execution. Splunk, on the other hand, is designed for real-time monitoring and analysis and is optimized for high-performance searching and indexing of data.
Ecosystem Integration: Apache Hive integrates well with the Apache Hadoop ecosystem and can work seamlessly with other tools like Apache Spark and Apache HBase. It leverages the Hadoop Distributed File System (HDFS) for data storage and can leverage other Hadoop components for data ingestion and processing. Splunk, on the other hand, provides its own ecosystem and has a wide range of integrations with third-party systems and tools.
Licensing Model: Apache Hive is an open-source project and is released under the Apache License. It can be freely used, modified, and distributed by anyone. Splunk, on the other hand, follows a commercial licensing model. While it offers a free version with limited features, the full functionality of Splunk requires a paid license.
In summary, Apache Hive is a data processing tool optimized for batch processing and working with structured data stored in Apache Hadoop, while Splunk is a real-time log management and analysis platform capable of handling various data sources. Apache Hive provides a SQL-like interface and integrates well with the Hadoop ecosystem, while Splunk offers a comprehensive web-based interface and extensive third-party integrations. Additionally, Apache Hive is open-source, while Splunk follows a commercial licensing model.
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
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The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.
Pros of Apache Hive
Pros of Splunk
- API for searching logs, running reports3
- Alert system based on custom query results3
- Splunk language supports string, date manip, math, etc2
- Dashboarding on any log contents2
- Custom log parsing as well as automatic parsing2
- Query engine supports joining, aggregation, stats, etc2
- Rich GUI for searching live logs2
- Ability to style search results into reports2
- Granular scheduling and time window support1
- Query any log as key-value pairs1
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Cons of Apache Hive
Cons of Splunk
- Splunk query language rich so lots to learn1
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What is Apache Hive?
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