when querying a table, a SerDe will deserialize a row of data from the bytes in the file to objects used internally by Hive to operate on that row of data. when performing an INSERT or CTAS (see “Importing Data” on page 441), the table’s SerDe will serialize Hive’s internal representation of a row of data into the bytes that are written to the output file.
Is serDe library?
How does hive store data i.e it stores in file or table?
Please can anyone explain the bold sentences clearly?
I'm new to hive!!
Answers
Yes, SerDe is a Library which is built-in to the Hadoop API
Hive uses Files systems like HDFS or any other storage (FTP) to store data, data here is in the form of tables (which has rows and columns).
SerDe - Serializer, Deserializer instructs hive on how to process a record (Row). Hive enables semi-structured (XML, Email, etc) or unstructured records (Audio, Video, etc) to be processed also. For Example If you have 1000 GB worth of RSS Feeds (RSS XMLs). You can ingest those to a location in HDFS. You would need to write a custom SerDe based on your XML structure so that Hive knows how to load XML files to Hive tables or other way around.
For more information on how to write a SerDe read this post
In this aspect we can see Hive as some kind of database engine. This engine is working on tables which are built from records.
When we let Hive (as well as any other database) to work in its own internal formats - we do not care.
When we want Hive to process our own files as tables (external tables) we have to let him know - how to translate data in files into records. This is exactly the role of SerDe. You can see it as plug-in which enables Hive to read / write your data.
For example - you want to work with CSV. Here is example of CSV_Serde
https://github.com/ogrodnek/csv-serde/blob/master/src/main/java/com/bizo/hive/serde/csv/CSVSerde.java
Method serialize will read the data, and chop it into fields assuming it is CSV
Method deserialize will take a record and format it as CSV.
Hive can analyse semi structured and unstructured data as well by using
(1) complex data type(struct,array,unions)
(2) By using SerDe
SerDe interface allow us to instruct hive as to how the record should be processed. Serializer will take java object that hive has been working on,and convert it into something that hive can store and Deserializer take binary representation of a record and translate into java object that hive can manipulate.
I think the above has the concepts serialise and deserialise back to front. Serialise is done on write, the structured data is serialised into a bit/byte stream for storage. On read, the data is deserialised from the bit/byte storage format to the structure required by the reader. eg Hive needs structures that look like rows and columns but hdfs stores the data in bit/byte blocks, so serialise on write, deserialise on read.
Related
I have the task to create kafka consumer that should extract messages from kafka, transfrom it and store into Hive table.
So, in kafka topic there are a lot of messages as json object.
I like to add some field and insert its into hive.
I create flow with following Nifi-processors:
ConsumeKafka_2_0
JoltTransformJSON - for transform json
ConvertRecord - to transform json into insert query for hive
PutHiveQL
The topic will be sufficiently loaded and handle about 5Gb data per day.
So, are the any ways to optimize my flow (i think it's a bad idea to give a huge amount of insert queries to Hive)? Maybe it will be better to use the external table and putHDFS Processor (in this way how to be with partition and merge input json into one file?)
As you suspect, using PutHiveQL to perform a large number of individual INSERTs is not very performant. Using your external table approach will likely be much better. If the table is in ORC format, you could use ConvertAvroToORC (for Hive 1.2) or PutORC (for Hive 3) which both generate Hive DDL to help create the external table.
There are also Hive streaming processors, but if you are using Hive 1.2 PutHiveStreaming is not very performant either (but should still be better than PutHiveQL with INSERTs). For Hive 3, PutHive3Streaming should be much more performant and is my recommended solution.
I am trying to analyze twitter data using flume
i got the files from twitter using flume in BigInsights
but the data I received is of compressed Avro schema which is not readable
can anyone tell me a way so that can convert that file to JSON (Readable)
in order to do some analysis on it.
Or is there any way so that the data I receive is already in JSON (Readable) format.
Thanks In Advance.
This is the data i received
Avro format is not designed to be human readable and it's desinged to be consumed by programs. But you have a few options to view this data or even better analyze the data.
Create Hive Table: This option will allow you to analyze data using SQL queries, Spark SQL, Spark notebooks, visualization tools like Tableau and Excel too.
Your table creation script will look like this:
CREATE TABLE twitter_data
ROW FORMAT
SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe'
STORED AS
INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat'
TBLPROPERTIES ('avro.schema.literal'='{...
In schema literal, you can define your own schema too.
Write Program: If you are developer and want to/like to wrangle data using programming, you have many languages to choose from to read, parse, convert and write from Avro file to JSON.
I have a bunch of tables in Hive, stored as ORC. I want to index their data in a SolrCloud collection.
Is there any support for indexing data stored in ORC format in Solr?
I've googled around but nothing came out.
Looks like you want SolR to read data from a specific Hive file format.
You might look at the problem the other way i.e. use Hive to write data to SolR -- and thus let Hive take care of the complexity of the actual input file format (whether ORC, Parquet, AVRO, whatever -- even HBase data files).
In the LucidWorks GitHub repo you will find a project labeled hive-solr. Have a look.
I'll accept Samson's answer.
Anyway, I'm not fully satisfied about this solution. In fact, now I still need to create an external table manually declaring all fields in the original table. In terms of operations, it is not different from creating a new table (stored ad textfile) starting from the original one, indexing the new text files and finally dropping them (of course, this may be a problem for very large tables, which is not my case).
Being ORC a self-describing format, it would be great for Solr to read both field names and data directly from the compressed files.
My Hbase table has rows that contain both serialized avro (put there using havrobase) and string data. I know that Hive table can be mapped to avro data stored in hdfs to do data analysis but I was wondering if anyone has tried to map hive to hbase table(s) that contains avro data. Basically I need to be able to query both avro and non avro data stored in Hbase, do some analysis and store the result in a different hbase table. I need the capability to do this as a batch job as well. I don't want to write a JAVA MapReduce job to do this because we have constantly changing configurations and we need to use a scripted approach. Any suggestions? Thanks in advance!
You can write an HBase co-processor to expose the avro record as regular HBase qualifiers. You can see an implementation of that in Intel's panthera-dot
I am using Amazon EMR Hadoop Hive for big data processing. Current data in my log files is in CSV format. In order to make the table from log files, I wrote regex expression to parse the data and store into different columns of external table. I know that SerDe can be used to read data in JSON format and this means that each log file line could be as JSON object. Are there any Hadoop performance advantages if my log files are in JSON format comparing CSV format.
If you can process the output of the table (that you created with the regexp) why do another processing? Try to avoid unnecessary stuff.
I think the main issue here is which format is faster to read. I believe CSV will provide better speed over JSON but don't take my word. Hadoop really doesn't care. It's all byte arrays to him, once in memory.