I am new on hadoop. I have data in tsv format with 50 columns and I need to store the data into hive. How can I create and load the data into table on the fly without manually creating table using create table statementa using schema on read?
Hive requires you to run a CREATE TABLE statement because the Hive metastore must be updated with the description of what data location you're going to be querying later on.
Schema-on-read doesn't mean that you can query every possible file without knowing metadata beforehand such as storage location and storage format.
SparkSQL or Apache Drill, on the other hand, will let you infer the schema from a file, but you must again define the column types for a TSV if you don't want everything to be a string column (or coerced to unexpected types). Both of these tools can interact with a Hive metastore for "decoupled" storage of schema information
you can use Hue :
http://gethue.com/hadoop-tutorial-create-hive-tables-with-headers-and/
or with Spark you can infer the schema of csv file and you can save it as a hive table.
val df=spark.read
.option("delimiter", "\t")
.option("header",true)
.option("inferSchema", "true") // <-- HERE
.csv("/home/cloudera/Book1.csv")
Related
i have one doubt, is there any way in HIVE which create table during load to hive warehouse or external table.
As i know hive is based on Schema On Read. so table structure must sync with file structure. but if file size is huge and we don't know its structure for example columns and their datatypes.
Than how to load those file to hive table.
so in short how to load file from HDFS to HIVE Table without knowing its schema structure.
New to Hive, Pardon if my understanding is wrong.
Thanks
By using sqoop you can create hive table while importing data.
Please refer to this link to create hive table while importing data
(or)
if you have imported data in AVRO format then you can generate avro schema by using
/usr/bin/Avro/avro-tools-*.jar then use the generated avro schema while creating table in hive then hive uses the schema and reads the data from HDFS.
Please refer to this link to extract schema from avro data file
(or)
While importing data using sqoop --as-avrodatefile then sqoop creates .avsc file with schema in it, so we can use this .avsc file creating the table.
CREATE EXTERNAL TABLE avro_tbl
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'
LOCATION '<hdfs-location>'
TBLPROPERTIES ('avro.schema.url'='<schema-file>');
(or)
By using NiFi to import data NiFi pulls data in avro format by using ExtractAvroMetadata processor we can extract the avro schema and store into HDFS and create table by using this avro schema.
If you want to create table in ORC format then by using ConvertAvroToOrc processor adds hive.ddl attribute to the flowfile as we can execute the ddl statement to create orc table in hive.
I have some data in hdfs.
This data was migrated from a PostgreSQL database by using Sqoop.
The data has the following hadoopish format, like _SUCCESS, part-m-00000, etc.
I need to create a Hive table based on this data and then I need to export this table to a single tab-separated file.
As far as I know, I can create a table this way.
create external table table_name (
id int,
myfields string
)
location '/my/location/in/hdfs';
Then I can save the table as tsv file:
hive -e 'select * from some_table' > /home/myfile.tsv
I don't know how to load data from hdfs into a Hive table.
Moreover, should I manually define the structure of a table using create or is there any automated way when all columns are created automatically?
I don't know how to load data from hdfs into Hive table
You create a table schema over a hdfs directory like you're doing.
should I manually define the structure of a table using create or is there any automated way when all columns are created automatically?
Unless you didn't tell sqoop to create the table, you must do it manually.
export this table into a single tab-separated file.
A query might work, or unless sqoop set the delimiter to \t, then you need to create another table from the first specifying such column separator. And then, you don't even need to query the table, just run hdfs dfs -getMerge on the directory
I added a CSV file in HDFS using R script.
I update this CSV with new CSV/append data to it
Created table using hue in Hive over this CSV.
Altered it to be an external table.
Now, if when data is changed in the hdfs location, would data be automatically updated in hive table?
That's the thing with external (and also managed) tables in Hive. They're not really tables. You can think of them as link to HDFS location. So whenever you query external table, Hive reads all the data from location you selected when you created this table.
From Hive doc:
An EXTERNAL table points to any HDFS location for its storage, rather
than being stored in a folder specified by the configuration property
hive.metastore.warehouse.dir.
I created a table in hive from data stored in hdfs with this command:
create external table users
(ID INT, NAME STRING, ADRESS STRING, EMAIL STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' STORED AS TEXTFILE LOCATION '/data/tpch/users';
This users table stored in hdfs has 10gb. And the create table just took 1second to create the table and load the data. So this is strange or it is really fast. My doubt is, to check the time of load tables with data in hive can be with that command above with location? Or that command just create a reference to data stored in hdfs?
So what is the correct way to check the time to load data in hive tables?
Because 1second seems really fast, mysql or another relational database probably need 30 or more minutes for load 10gb of data into a table.
Your create table statement is pointing to external storage for the tables, so Hive is not copying the data over. The documentation explains external tables like this:
External Tables
The EXTERNAL keyword lets you create a table and provide a LOCATION so
that Hive does not use a default location for this table. This comes
in handy if you already have data generated. When dropping an EXTERNAL
table, data in the table is NOT deleted from the file system.
An EXTERNAL table points to any HDFS location for its storage, rather
than being stored in a folder specified by the configuration property
hive.metastore.warehouse.dir.
This is not 100% explicit, but the idea is that Hive is pointing to the table contents rather than managing it directly.
I am setting up a new hadoop cluster (experimental at this stage).
I want it to be configured such that whenever a file is copied onto the cluster (either through copyFromLocal or using sqoop etc), hadoop/hdfs should store the data in parquet file format.
Am I expecting right about this ? is it possible ?
I thought there should be a configuration parameter somewhere at the hdfs level, where i could specify which format to use while storing data, somehow not able to find that. Wondering if i m missing something here.
No you're right - there's no HDFS-level configuration. You'd have to set the storage format each time you operate on some data. Imagine the damage that would be done if every file was automatically converted into Parquet. All the temporary files created by applications, any Hive/Pig scripts and any lookup files would be ruined.
To save the output of a Sqoop command into Parquet:
sqoop import --connect JDBC_URI --table TABLE --as-parquetfile --target-dir /path/to/files
will write the data into Parquet format.
There's no way to do this with a copyFromLocal.
To move data that's already on the HDFS into Parquet, load the data into an external Hive table in its original format, create a Parquet table and then load the data into it, i.e.
//Overlay a table onto the input data on the HDFS
CREATE EXTERNAL TABLE input (
id int,
str string
STORED AS <the-input-data-format>
LOCATION 'hdfs://<wherever-you-put-the-data>';
//Create a Parquet-formatted table
CREATE TABLE parquet (
id int,
str string
STORED AS PARQUET;
//Write your input data into the Parquet table - this will format the data into Parquet
INSERT INTO TABLE parquet
SELECT * FROM input;