Pros y Contras de Hive

nada en particular. nos ayuda con los grandes datos y permite a todos los usuarios tener ancho de banda sin restricciones, pero ya nos encontramos con problemas con eso, así que ahora uno de los servidores tiene limitaciones. Reseña recopilada por y alojada en G2.com.

. en mi empresa fue bastante problemático obtener acceso ya que su almacenamiento subyacente está en hadoop, luego hay que conectarse a través de hive Reseña recopilada por y alojada en G2.com.

I was a top fan of Impala for a while until I reached a series of limitations that were impossible to overcome. I work a lot with arrays and just the fact of being able to use array_contains in impala made me switch to Hive. Also, we are moving fast on the direction of self made Macross for hive that let us do complex queries without lateral view explodes Reseña recopilada por y alojada en G2.com.

Session creation takes a while and speed is quite slow when comparing to Impala Reseña recopilada por y alojada en G2.com.

It is highly flexible in configurations. So many options to load data from- directly from linux file system or hdfs.

You can create external and managed tables. One fun feature is that you can shoot bash commands from hive as well Reseña recopilada por y alojada en G2.com.

It cannot be used for streaming data. Error logging can be improved so that error tracking and resolution can be more efficient. Reseña recopilada por y alojada en G2.com.

- Easy to use interface

- multiple clients (CLIs)

- easy to debug issues with the help of fully descriptive logs

- constantly the product is being improved to meet all the DB developer requirements

- can be accessed from multiple applications

- access through knox for additional security

- no indexing

- multiple file formats

- the tez architecture Reseña recopilada por y alojada en G2.com.

- authentication gaps

- issues when routing through zookeeper

- not as matured tool as the regular database tools Reseña recopilada por y alojada en G2.com.

Facilidad para comenzar. Aprovecha el conocimiento de SQL. Tiene documentación razonable. Rápido para escribir consultas. Reseña recopilada por y alojada en G2.com.

La documentación es escasa en algunas áreas, como los formatos de fecha y hora. Las consultas se ejecutan lentamente y a menudo no se completan. Reseña recopilada por y alojada en G2.com.

The best thing about HIVE is that anyone that is familiar with SQL can take advantage of HIVE's ability to run map reduce jobs. Newer version of HIVE is getting better at supporting windowing functions and fleshing out any inconsistencies. So far the documentation is good enough for getting me through my tasks and there is still on-going support for this product, which is a pretty good sign to me. Reseña recopilada por y alojada en G2.com.

Older versions of HIVE sucks. There are lots of limitations that will force you to write HiveQL queries that are not straight forward and, even potentially, inefficient. For example, no support for window functions and no equality comparisons on joins can make your life very difficult so you will need to fall back to using some whacky full joins or self joins to accomplish the same task. Reseña recopilada por y alojada en G2.com.

Hive is the best out there for answering ad-hoc queries in parallel paradigm. It works very well with Hadoop Echo system (mainly integrates perfectly with HDFS).

- Easy to use as it implements most of SQL functions. Reseña recopilada por y alojada en G2.com.

- Needs more optimization for complex queries (like caching, auto-partitioning,etc ...) to speed up the latency of the queries.

- Tuning the hive parameters is really challenging for the users. The default settings don't work with the large queries.

- Hive is perfect if 90-95% of the queries are read-only. It is not suitable for applications with heavily updates Reseña recopilada por y alojada en G2.com.

Hive is great for handling logs in big data projects. We are using the same in our project and it is great for using joins and grouping which is very difficult and tricky in map reduce. It has a lot of udf packages and it is very easy to add new udfs. We were also using bucketing and clustering to optimize the query. Concept of external tables and the way we can manipulate data even when table is deleted from hive is really amazing. Lot of connectors available in the market for different softwares. Reseña recopilada por y alojada en G2.com.

The thing which I dislike is latency and the way it saves data. While inserting data I have to wait a lot of few records. Compiler execution plan is very immature as it does not do proper query optimization. Though the community is working fast for overcoming quickly but I think it will take time for hive to be Reseña recopilada por y alojada en G2.com.

If you are data analyst and expert in SQL then use Hive. Hive is very easy to work with especially if you are a SQL person.

I use both hive and pig at work. I use hive mainly for ad hoc quires and reports. For BI reports Hive is the best since you can reuse all the SQL that you have done for traditional data warehouses. Also with Hive Server2 you get a real JDBC support so you can plug your BI tools to it. Many more SQL features like cubes, rollups, windowing, lag, lead, etc are being added to Hive through Hortonworks Stinger initiative. Hive also produces very compact code, which is always good for reading and debugging. Reseña recopilada por y alojada en G2.com.

I would suggest to use hive for large projects, where you want to implement SQL-like data access, schemas, metadata, partitions, server-based deployment, jdbc, etc.

Pig is a good language and can be very handy for immediate tasks or small projects. i would recommend PIG for small projects . Reseña recopilada por y alojada en G2.com.

Leverage sql skills to perform operations on data stored in hadoop. Reseña recopilada por y alojada en G2.com.

Works on map reduce algorithm, so the retrieval of data is a little slow. Reseña recopilada por y alojada en G2.com.