Difference Between 94 95 96 Impala Ss

[Aureo Harvey]

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Apache Hadoop Hive is an effective standard for SQL in Apache Hadoop. Hadoop Hive forms the front-end to parse SQL statements, to generate and optimize logical plans, translating these logical plans to physical plans which are then executed by various other MapReduce jobs. Hadoop Hive is designed to cater the data warehouse systems to ease the whole process of ad-hoc queries on Big Data stored in HDFS filesystems.

Apache Hive is undoubtedly the slowest in comparison with Cloudera Impala, but Apache Hive is a great option for heavy ETL jobs where reliability plays an important role. Impala is an open source SQL engine to process queries on huge volumes of data providing a very good performance over Apache Hadoop Hive.

Impala is way better than Hive but this does not qualify to say that it is a one-stop solution for all the Big Data problems. Impala is a memory intensive technology and performance driven technology. It does not run effectively for heavy data operations like joins as not everything can be pushed into the memory. If there is an application that has batch processing kind of needs, then those organizations should be opting Hive over Impala as Hive suites such a need more efficiently than Impala.

Big Data keeps getting bigger. It continues to pressurize existing data querying, processing, and analytic platforms to improve their capabilities without compromising on the quality and speed. A number of comparisons have been drawn and they often present contrasting results. Cloudera Impala and Apache Hive are being discussed as two fierce competitors vying for acceptance in database querying space. While Hadoop has clearly emerged as the favorite data warehousing tool, the Cloudera Impala vs Hive debate refuses to settle down.

In this article, we have tried to understand what both of these technologies namely Hadoop Hive and Cloudera Impala are and also understood details about these technologies in detail. We have tried to showcase few differences between these two technologies but in practice, these are not two different competitors competing to show which one of them is the best, but each complements other in rarely good use cases but each of them is known for their characteristics as defined earlier.

A springbok is a type of antelope that inhabits the southwestern and southern areas of Africa. It belongs to the Antidorcas genus and is further divided into 3 subspecies. This antelope species is primarily active during the early morning and early evening hours when they eat shrubs and succulent plants with the rest of their herd. Its conservation status is listed by the IUCN as of least concern, which means there is no immediate threat to its survival. In fact, records indicate that the springbok population size is growing.

An impala is a type of antelope that inhabits the eastern and southern areas of Africa. It belongs to the Aepyceros genus and is further divided into 2 subspecies. This antelope species is primarily active during the day when it grazes on fruits and other vegetation with one of three types of herds: bachelor, female, or territorial male. Its conservation status is listed by the IUCN as of least concern. However, the black-faced subspecies has been listed as vulnerable. This subspecies has a population size of less than 1,000.

For example, the size and appearance of these species are different. Both sexes of the springbok grow to between 28 and 34 inches in height and 47 to 59 inches in length. It can weigh anywhere from 60 to 93 pounds and has a 5.5 to 11-inch tail. Its tail is distinguished by a black tuft of fur at the tip. Additionally, both sexes grow 14 to 20-inch long black, curved horns. Each of the subspecies has a different color, including: dark brown with a black side stripe, tan with a black side stripe, and white with a tan side stripe.

The impala, in contrast, has a difference in size based on sex. The male grows to between 30 and 36 inches in height, while the female grows to between 28 and 32 inches. The males weigh between 117 and 168 pounds and the female between 88 to 117 pounds. Unlike female springboks, female impalas do not grow horns. Male impalas grow 18 to 36-inch long dark, curled horns. Its coloration is a reddish brown on top with tan sides and hind legs and a white stomach.

As previously mentioned, the springbok is active around sunrise and sunset, primarily in mixed sex herds. The springbok is recognized for its unique leaping style, called pronking. It can jump up to 6.6 feet into the air while bending its back, but not its legs. The springbok can also survive for years without consuming water as it obtains sufficient fluids from the vegetation (like succulents) that make up its diet. Its breeding season can happen during any time of the year.

The impala, active during the day, maintains one of three distinct herd types. Researchers believe this is the only antelope species to participate in allogrooming, which is grooming of other adults. The impala can jump up to 9.8 feet in the air and up to 33 feet in distance. Its breeding season occurs once a year for 3 weeks, usually in May.

Coming from a DWH-background I am used to putting subqueries almost everywhere in my queries. On a Hadoop project (with Hive version 1.1.0 on Cloudera), I noticed we can forego subqueries in some cases.

It made me wonder if there are similar SQL-dialect specific differences between what is used in Hadoop SQL and what you would use in a DWH-setting. So I would like to extend this question so that people can mention what they noticed as differences between Hadoop and DWH in when structuring their queries. I noticed there was very little reference to this topic for Hadoop.

It would be nice to get a few of your best practices for working with Hadoop. E.g. You write your queries as neutral as possible so that it works in both Hive and Impala avoiding using language-specific functions such as left (Impala only)

Consider what impact changes to settings might have. E.g. yarn.nodemanager.resource.memory-mb=24576 is great to use, but what happens if you are not allowed to change the node memory size. In an automated job it is not necessarily good practice. Look at the general Hadoop settings, e.g. file size.

Avoid using functions that are specific to a program. E.g. select left("Hello world", 3) is useful in Impala but in Hive it has to be rewritten to select left("Hello world", 1,4). This can lead to problems when later on down the line it will run in a different program.

Temp tables are your friend - prior to hadoop, I avoided staging data in temporary tables a lot and wrote more complex SQL to avoid it. But with Hadoop and big data, I've actually found it much faster to create tables and then subsequently joining to them helps in some cases with massive data.

How the data is stored (skewed or not, bucketed or not, etc) makes a huge different. Keep an eye out for queries where you have 100 reducers and 99 of them finish super fast and 1 of them takes forever.

Like I mentioned, spend time learning how the execution engines do the work in a distributed environment. Once you understand how the execution happens behind the scenes, you can start to pick up on other subtleties like why this:

I recently wrote a blog post about Oracle's Analytic Views and how those can be used in order to provide a simple SQL interface to end users with data stored in a relational database. In today's post I'm expanding a little bit on my horizons by looking at how to effectively query data in Hadoop using SQL. The SQL-on-Hadoop interface is key for many organizations - it allows querying the Big Data world using existing tools (like OBIEE,Tableau, DVD) and skills (SQL).

Analytic Views, together with Oracle's Big Data SQL provide what we are looking for and have the benefit of unifying the data dictionary and the SQL dialect in use. It should be noted that Oracle Big Data SQL is licensed separately on top of the database and it's available for Exadata, SuperCluster, and 12c Linux Intel Oracle Databases machines only.

Nowadays there is a multitude of open-source projects covering the SQL-on-Hadoop problem. In this post I'll look in detail at two of the most relevant: Cloudera Impala and Apache Drill. We'll see details of each technology, define the similarities, and spot the differences. Finally we'll show that Drill is most suited for exploration with tools like Oracle Data Visualization or Tableau while Impala fits in the explanation area with tools like OBIEE.

As we'll see later, both the tools are inspired by Dremel, a paper published by Google in 2010 that defines a scalable, interactive ad-hoc query system for the analysis of read-only nested data that is the base of Google's BigQuery. Dremel defines two aspects of big data analytics:

We started blogging about Impala a while ago, as soon as it was officially supported by OBIEE, testing it for reporting on top of big data Hadoop platforms. However, we never went into the details of the tool, which is the purpose of the current post.

One of the performance improvements is related to "Streaming intermediate results": Impala works in memory as much as possible, writing on disk only if the data size is too big to fit in memory; as we'll see later this is called optimistic and pipelined query execution. This has immediate benefits compared to standard MapReduce jobs, which for reliability reasons always writes intermediate results to disk.

As per this Cloudera blog, the usage of Impala in combination with Parquet data format is able to achieve the performance benefits explained in the Dremel paper.

Impala runs a daemon, called impalad on each Datanode (a node storing data in the Hadoop cluster). The query can be submitted to any daemon in the cluster which will act as coordinator node for the query. Impala daemons are always connected to the statestore, which is a process keeping a central inventory of all available daemons and related health and pushes back the information to all daemons. A third component called catalog service checks for metadata changes driven by Impala SQL in order to invalidate related cache entries. Metadata are cached in Impala for performance reasons: accessing metadata from the cache is much faster than checking against the Hive metastore. The catalog service process is in charge of keeping Impala's metadata cache in sync with the Hive metastore.

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