I have a requirement to load Avro file to hive. Using the following to create the table
create external table tblName stored as avro location 'hdfs://host/pathToData' tblproperties ('avro.schema.url'='/hdfsPathTo/schema.avsc');
I am getting an error FOUND NULL, EXPECTED STRING while doing a select on the table. Is it possible to load few columns and find which column data is causing this error?
Actually you need first to create an Hive External table pointing to the location of your AVRO files, and using the AvroSerDe format.
At this stage, nothing is loaded. The external table is just a mask on files.
Then you can create an internal HIVE table and load data (the expected columns) from the external one.
If you are already having AVRO file, then load the file to HDFS in a directory of your choice. Next create an external table on top of the directory.
CREATE EXTERNAL TABLE external_table_name(col1 string, col2 string, col3 string ) STORED AS AVRO LOCATION '<HDFS location>';
Next create an internal hive table on top of the external table to load the data
CREATE TABLE internal_table_name(col2 string, col3 string) AS SELECT col2, col3 FROM external_table_name
You can schedule the internal table load using a batch script in any scripting language or tools.
Hope this helps :)
Related
I have an HDFS directory structure like this:
/home/date_1/A/file.txt
/home/date_1/B/file.txt
/home/date_2/A/file.txt
/home/date_2/B/file.txt
...
I can create an external table
CREATE EXTERNAL TABLE table_name(col1 int, col2 string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORE AS TEXTFILE
LOCATION '/home'
But I don't know how to introduce the folder information 'A' or 'B' into the table. What can I do? Thanks!
In Hive you have virtual columns which you can use to read the underlying filename. INPUT__FILE__NAME will give your the list of files that the data has used to get the filename.
So you need to first create external table (as you have done). Then when you query the external table you can make use of the virtual column and split the data, as below:
select
col1,
col2,
INPUT__FILE__NAME as full_filepath,
concat_ws("/",reverse(split(reverse(INPUT__FILE__NAME),"/")[1]), reverse(split(reverse(INPUT__FILE__NAME),"/")[0])) as splitted_filepath
FROM
table_name;
More on virtual column in hive.
Are you using MapReduce as the Hive execution engine? You should be able to simply direct the framework to traverse all the sub-directories.
SET mapreduce.input.fileinputformat.input.dir.recursive=true;
SET hive.mapred.supports.subdirectories=true;
SELECT COUNT(1) FROM table_name;
I am using databricks community edition.
I am using a hive query to create an external table , the query is running without any error but the table is not getting populated with the specified file that has been specified in the hive query.
Any help would be appreciated .
from official docs ... make sure your s3/storage location path and schema (with respects to the file format [TEXT, CSV, JSON, JDBC, PARQUET, ORC, HIVE, DELTA, and LIBSVM]) are correct
DROP TABLE IF EXISTS <example-table> // deletes the metadata
dbutils.fs.rm("<your-s3-path>", true) // deletes the data
CREATE TABLE <example-table>
USING org.apache.spark.sql.parquet
OPTIONS (PATH "<your-s3-path>")
AS SELECT <your-sql-query-here>
// alternative
CREATE TABLE <table-name> (id long, date string) USING PARQUET LOCATION "<storage-location>"
I am trying to migrate the data from Hive to BigQuery. Data in Hive table is stored in PARQUET file format.Data type of one column is STRING, I am uploading the file behind the Hive table on Google cloud storage and from that creating BigQuery internal table with GUI. The datatype of column in imported table is getting converted to BYTES.
But when I imported CHAR of VARCHAR datatype, resultant datatype was STRING only.
Could someone please help me to explain why this is happening.
That does not answer the original question, as I do not know exactly what happened, but had experience with similar odd behavior.
I was facing similar issue when trying to move the table between Cloudera and BigQuery.
First creating the table as external on Impala like:
CREATE EXTERNAL TABLE test1
STORED AS PARQUET
LOCATION 's3a://table_migration/test1'
AS select * from original_table
original_table has columns with STRING datatype
Then transfer that to GS and importing that in BigQuery from console GUI, not many options, just select the Parquet format and point to GS.
And to my surprise I can see that the columns are now Type BYTES, the names of the columns was preserved fine, but the content was scrambled.
Trying different codecs, pre-creating the table and inserting still in Impala lead to no change.
Finally I tried to do the same in Hive, and that helped.
So I ended up creating external table in Hive like:
CREATE EXTERNAL TABLE test2 (col1 STRING, col2 STRING)
STORED AS PARQUET
LOCATION 's3a://table_migration/test2';
insert into table test2 select * from original_table;
And repeated the same dance with copying from S3 to GS and importing in BQ - this time without any issue. Columns are now recognized in BQ as STRING and data is as it should be.
I am now preparing to store data in .csv files into hive. Of course, because of the good performance of parquet file format, the hive table should is parquet format. So, the normal way, is to create a temp table whose format is textfile, then I load local CSV file data into this temp table, and finally, create a same-structure parquet table and use sql insert into parquet_table values (select * from textfile_table);.
But I don't think this temp textfile table is necessary. So, my question is, is there a way for me to load these local .csv files into hive parquet-format table directly, namely, not to resort the a temp table? Or a easier way to accomplish this task?
As stated in Hive documentation:
NO verification of data against the schema is performed by the load command.
If the file is in hdfs, it is moved into the Hive-controlled file system namespace.
You could skip a step by using CREATE TABLE AS SELECT for the parquet table.
So you'll have 3 steps:
Create text table defining the schema
Load data into text table (move the file into the new table)
CREATE TABLE parquet_table AS SELECT * FROM textfile_table STORED AS PARQUET; supported from hive 0.13
At my firm, I see these two commands used frequently, and I'd like to be aware of the differences, because their functionality seems the same to me:
1
create table <mytable>
(name string,
number double);
load data inpath '/directory-path/file.csv' into <mytable>;
2
create table <mytable>
(name string,
number double);
location '/directory-path/file.csv';
They both copy the data from the directory on HDFS into the directory for the table on HIVE. Are there differences that one should be aware of when using these? Thank you.
Yes, they are used for different purposes at all.
load data inpath command is use to load data into hive table. 'LOCAL' signifies that the input file is on the local file system. If 'LOCAL' is omitted then it looks for the file in HDFS.
load data inpath '/directory-path/file.csv' into <mytable>;
load data local inpath '/local-directory-path/file.csv' into <mytable>;
LOCATION keyword allows to point to any HDFS location for its storage, rather than being stored in a folder specified by the configuration property hive.metastore.warehouse.dir.
In other words, with specified LOCATION '/your-path/', Hive does not use a default location for this table. This comes in handy if you already have data generated.
Remember, LOCATION can be specified on EXTERNAL tables only. For regular tables, the default location will be used.
To summarize,
load data inpath tell hive where to look for input files and LOCATION keyword tells hive where to save output files on HDFS.
References:
https://cwiki.apache.org/confluence/display/Hive/GettingStarted
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
Option 1: Internal table
create table <mytable>
(name string,
number double);
load data inpath '/directory-path/file.csv' into <mytable>;
This command will remove content at source directory and create a internal table
Option 2: External table
create table <mytable>
(name string,
number double);
location '/directory-path/file.csv';
Create external table and copy the data into table. Now data won't be moved from source. You can drop external table but still source data is available.
When you drop an external table, it only drops the meta data of HIVE table. Data still exists at HDFS file location.
Have a look at this related SE questions regarding use cases for both internal and external tables
Difference between Hive internal tables and external tables?