Diving Deep into Apache Hive vs Apache Impala

Hey there! If you‘re like me, you probably deal with large amounts of data on a regular basis. As data volumes continue to grow, it becomes more critical to use the right tools to store and analyze your data efficiently.

You may have heard about Apache Hive and Apache Impala – two leading technologies that allow querying Big Data using SQL. In this guide, I‘ll compare Hive and Impala in depth so you can decide which one better fits your use case. Buckle up – we have a lot to cover!

A Quick Intro to Hive and Impala

Let‘s start with a fast overview of what Hive and Impala are all about.

Apache Hive was created by Facebook engineers way back in 2010 as the first SQL interface for querying data stored in the Hadoop Distributed File System (HDFS). Hive lets you use a SQL-like language called HiveQL to perform analytics on Big Data, similar to how you would query a traditional database using SQL.

Apache Impala came later in 2012 as an open source project from Cloudera. It offered higher performance for interactive SQL queries on Hadoop, using a massively parallel processing (MPP) architecture.

The key difference is that Impala was designed from ground up for low latency queries, while Hive originally focused on batch jobs over huge datasets.

Over time, both Hive and Impala have evolved to take advantage of the best of both worlds – performance and scalability. Let‘s look at both in more detail.

Diving Into Hive Architecture

Hive is made up of multiple components that work together:

Hive Client – This is the interface for end users to submit queries and manage Hive. There are different clients available, like the Hive CLI, Beeline, web UI, and Thrift server for external connectivity.
Metastore – Basically a database that stores metadata about Hive tables and partitions. It is commonly hosted on a MySQL or Postgres database server. Did you know the Metastore is also leveraged by Impala?
Driver – This critical component compiles HiveQL queries into a series of tasks to be executed on the cluster. It includes the query optimizer that enforces performance best practices.
Execution Engine – Finally, the parsed tasks are executed on the Hadoop cluster via MapReduce, Tez or Spark jobs. Hive integrates natively with these processing frameworks.

One cool thing about Hive is that it provides extensive fault tolerance capabilities. Failed MapReduce jobs are automatically retried, and intermediate data is reused in case of retries or failures. This provides reliability for long running jobs, pretty useful when you‘re processing huge datasets!

Impala‘s Modern MPP Architecture

Impala employs a radically different massively parallel processing (MPP) architecture for high concurrency. Let‘s examine its core daemons:

impalad – This daemon runs on each data node in the cluster and takes care of actual query execution. They run in parallel across nodes for some serious performance benefits!
statestored – An intra-cluster service that tracks location and status of all impalad instances spread across the cluster. Keeps things in sync.
catalogd – Manages metadata updates from Impala DDL statements and broadcasts them to all Impala nodes automatically.

This modern MPP design allows Impala to bypass MapReduce and directly access data via HDFS caching for low latency. The daemons effectively distribute query execution across nodes in parallel.

The downside is Impala does not store intermediate results, so query failures could require a restart. However, its architecture is optimized to provide maximum performance for interactive queries where latency is a key factor.

Comparing Query Performance

Let‘s now dive into query performance, one of the most critical differences between Impala and Hive.

Independent benchmarks showed that Impala can outperform Hive by up to 25x on Hadoop and 150x over traditional databases. These numbers clearly highlight why Impala has become the tool of choice for fast query performance.

Impala achieves this speedup by avoiding the overhead of MapReduce. It parallelizes queries across nodes and uses runtime code generation for efficiency.

However, Hive can perform better for long, complex analytical queries. Hive is designed to handle multiple sequential jobs efficiently by reusing data between stages. Jobs are parallelized across the cluster for scalability.

Recent versions of Hive bumped up performance via the Live Long and Process (LLAP) capability. LLAP enables caching queries in memory and uses persistent daemons for better concurrency. This helped Hive achieve much better response times.

So in summary, for interactive queries on small-to-medium data, Impala provides superior performance. But Hive offers scalability for complex jobs on huge datasets.

Comparing Use Cases

Given their architectural differences, Hive and Impala are each better suited for certain use cases:

Hive – Best used for long running batch ETL jobs because it can efficiently handle huge volumes of data. Commonly used for data warehousing tasks like reporting where SQL access is preferred.
Impala – Optimized for faster response times on interactive SQL queries needed for exploratory analytics. An ideal fit for BI analysts who need to query data on the fly for dashboards or reports.

Personally, I rely on Impala for rapid analysis to slice and dice data on my laptop. But for our monthly data pipeline that transforms hundreds of gigabytes, nothing beats Hive‘s scalability!

SQL Dialect and Compatibility

Both Hive and Impala use SQL dialects for querying data in Hadoop. There are some key differences:

HiveQL – Hive‘s SQL variant adds extensions like multitable inserts to optimize for MapReduce jobs. There are also Hive-specific functions and syntax not in standard SQL.
Impala SQL – Impala syntax is closer to standard SQL for portability but does not fully comply. It uses HiveQL syntax but adds analytic functions and drops some HiveQL features.

In my experience, simple HiveQL queries generally work unchanged in Impala. But more complex queries, especially those with MapReduce oriented constructs, may not port directly. Always test your queries before assuming compatibility.

Security – A Critical Consideration

Security is a critical requirement especially for enterprises adopting Hadoop. Both Impala and Hive have security capabilities:

Impala – Supports Kerberos authentication for secure access. Also integrates with Sentry for role-based authorization of queries and data.
Hive – Relies on underlying HDFS permissions rather than managing security itself. HiveServer2 does support Kerberos for authentication. Authorization requires integration with Sentry.

If your environment mandates Kerberos or Sentry based security, Impala saves you effort with its built-in integration. For Hive, you need to explicitly configure security plugins.

How File Formats Affect Queries

The file format used to store data can impact query performance. Both Impala and Hive support columnar formats like Parquet and Avro that are optimized for analytics:

Impala – Works well with Parquet as statistics help optimize query plans. Also supports Avro, RCFile, SequenceFile.
Hive – ORC file format provides the best compression and performance. Also handles Parquet, Avro, RCFile via libraries.

My preference is Parquet, as it provides good compression and performance for both tools. I‘ve seen queries run 2-3x faster on Parquet relative to text or CSV data.

Key Differences at a Glance

Let‘s recap the key differences between the two tools:

Feature	Apache Impala	Apache Hive
Language	C++	Java
Latency	Low	High
Use Case	Interactive queries	Batch ETL
Performance	Fast for concurrency	Scales for complexity
Security	Kerberos & Sentry built-in	Requires plugins
File Format	Parquet best	ORC best

Using Hive and Impala Together

Hive and Impala actually complement each other nicely for a comprehensive SQL-on-Hadoop solution. Here are two great ways to use them together:

Use Hive for ETL ingestion to clean, transform and load new data into Hive tables
Then Impala can directly query the same tables for interactive analysis
Impala can rapidly query and combine data for real-time dashboards and reports
Batch jobs in Hive can run complex transformations on the same data

This demonstrates how Hive and Impala pair up to support different needs – from initial data ingestion to interactive analysis and dashboards.

The Final Verdict

So which tool would I recommend? The answer – as always – depends on your specific needs:

For complex ETL or orchestrating jobs on huge datasets, Hive is your best bet
If your workload requires speedy queries for real-time analytics, Impala is the clear winner

Personally, I leverage both in my environment. Hive handles long running monthly ETL pipelines while Impala powers ad-hoc analysis and dashboards.

The bottom line is that Hive and Impala are both extremely capable in their own ways. I hope this detailed comparison helped you understand which one is a better fit for your use case. Feel free to reach out if you have any other questions!