Skip to main content

Hadoop based SQL engines

By Anonymous
Listen:

Apache Hadoop comes more and more into the focus of business critical architectures and applications. Naturally SQL based solutions are the first to get considered, but the market is evolving and new tools are coming up, but leaving unnoticed.

Listed below an overview over currently available Hadoop based SQL technologies. The must haves are:

Open Source (various contributors), low-latency querying possible, supporting CRUD (mostly!) and statements like CREATE, INSERT INTO, SELECT * FROM (limit..), UPDATE Table SET A1=2 WHERE, DELETE, and DROP TABLE.

Apache Hive (SQL-like, with interactive SQL (Stinger)
Apache Drill (ANSI SQL support)
Apache Spark (Spark SQL, queries only, add data via Hive, RDD or Parquet)
Apache Phoenix (built atop Apache HBase, lacks full transaction support, relational operators and some built-in functions)
Presto from Facebook (can query Hive, Cassandra, relational DBs & etc. Doesn't seem to be designed for low-latency responses across small clusters, or support UPDATE operations. It is optimized for data warehousing or analytics¹)
VoltDB (ACID compatible, ANSI SQL near 92, low-latency multi query engine)
SQL-Hadoop via MapR community edition (seems to be a packaging of Hive, HP Vertica, SparkSQL, Drill and a native ODBC wrapper)
Apache Kylin from Ebay (provides an SQL interface and multi-dimensional analysis [OLAP], "… offers ANSI SQL on Hadoop and supports most ANSI SQL query functions". It depends on HDFS, MapReduce, Hive and HBase; and seems targeted at very large data-sets though maintains low query latency)
Apache Tajo (ANSI/ISO SQL standard compliance with JDBC driver support [benchmarks against Hive and Impala])
Cascading's Lingual² ("Lingual provides JDBC Drivers, a SQL command shell, and a catalog manager for publishing files [or any resource] as schemas and tables.")

Non Open Source, but also interesting
Splice Machine (Standard ANSI SQL, Transactional Integrity)
Pivotal Hawq (via Pivotal HD, ANSI SQL 92, 99 and OLAP)
Cloudera Impala (SQL-like, ANSI 92 compliant, MPP and low-latency)
Impala does not incorporate usage of Hadoop, but leverages the cached data of HDFS on each node to quickly return data (w/o performing Map/Reduce jobs). Thus, overhead related to performing a Map/Reduce job is short-cut and one can gain improvements runtime.
Conclusion: Impala does not replace Hive. However, it is good for different kind of jobs, such as small ad-hoc queries well-suited for analyzing data as business analysts. Robust jobs such as typical ETL taks, on the other hand, require Hive due to the fact that a failure of one job can be very costly.

Thanks to Samuel Marks, who posted originally on the hive user mailing list
Labels:

Comments

Post a Comment

Popular posts from this blog

Beyond Ctrl+F - Use LLM's For PDF Analysis

By
PDFs are everywhere, seemingly indestructible, and present in our daily lives at all thinkable and unthinkable positions. We've all got mountains of them, and even companies shouting about "digital transformation" haven't managed to escape their clutches. Now, I'm a product guy, not a document management guru. But I started thinking: if PDFs are omnipresent in our existence, why not throw some cutting-edge AI at the problem? Maybe Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) could be the answer. Don't get me wrong, PDF search indexes like Solr exist, but they're basically glorified Ctrl+F. They point you to the right file, but don't actually help you understand what's in it. And sure, Microsoft Fabric's got some fancy PDF Q&A stuff, but it's a complex beast with a hefty price tag. That's why I decided to experiment with LLMs and RAG. My idea? An intelligent knowledge base built on top of our existing P...
Post a Comment
Read more

Deal with corrupted messages in Apache Kafka

By
Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this in...
Post a Comment
Read more

Why Is Customer Obsession Disappearing?

By
 It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers.The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and now we're seeing the problems that came with it. "Cases" What Not to Do Coinbase, as main example, has long been synonymous with making cryptocurrency accessible. Whether you’re a first-time buyer or a seasoned trader, their platform was once the gold standard for user experience. But lately, their customer support practices have been making headlines for all the wrong reasons: Coinbase - Stuck in the Loop:  Users have reported being caugh...
Post a Comment
Read more