I created below mention external table..
create external table if not exists sensor.building1 (BuildingID int,BuildingMgr string , BuildingAge string, HVACproduct string , Country string) row format delimited fields terminated by ',';
Loaded the table by using below query..
load data inpath '/user/cloudera/sensor/SensorFiles/building.csv' into table sensor.building1;
When I am trying to retrieve the buildingID column using below query, but I am getting null value..
select a.BuildingID
from sensor.building1 as a
limit 10;
Please guide me where I am doing something wrong
You are trying to load a CSV file into hive table but hive's default field delimiter is '\001'
So while you tring to load data from csv (I am assuming its ',' separated) its get failed.
You can create table like :
create external table test1(country string, name string) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n';
Related
I am new to Hive and am creating external tables on csv file. One of the issues I am coming across are values that contain multiple commas within string itself. For example, the csv file contains the following:
CSV File
When I create an external table in Hive, because there are columns within the "name" column, it shifts the first name to the right adding another column. This throws all of the data off when you view the table in Hive.
External Table result in Hive
Is there anything I can add to my script to keep the commas but also keep first and last name in the same column when the external table is created? Thank you all in advance - I am very new to Hive.
CREATE EXTERNAL TABLE database.table name (
ID INT,
Name String,
City String,
State String
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/xyz/xyz/database/directory/'
TBLPROPERTIES ("skip.header.line.count"="1");
Check this solution - you need to add this line : ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
https://community.cloudera.com/t5/Support-Questions/comma-in-between-data-of-csv-mapped-to-external-table-in/td-p/220193
Complete DDL example:
create table hcc(field1 string,
field2 string,
field3 string,
field4 string,
field5 string)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
WITH SERDEPROPERTIES (
"separatorChar" = ",",
"quoteChar" = "\"");
I am looking to encode columns of a table in hive.
I tried:
hive> create table encode_test(id int, name STRING, phone STRING, address STRING)
> ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
> WITH SERDEPROPERTIES ('column.encode.columns'='phone,address', 'column.encode.classname'='org.apache.hadoop.hive.serde2.Base64WriteOnly') STORED AS TEXTFILE;
Say i have a CSV file, with following row
100,'navis','010-0000-0000','Seoul Seocho'
Now i tried to use.
LOAD DATA LOCAL INPATH
'/home/path/to/csv/test.csv'
INTO TABLE encode_test;
But when doing Select * from encode_test i am getting all columns NULL
Whereas the result should have been
100 navis MDEwLTAwMDAtMDAwMA== U2VvdWwsIFNlb2Nobw==
Also i want to give Fields TERMINATED BY ',' IN create table encode_test query.
but i am getting error: EOF error Near Fields
I also tried creating another table sample
create table sample(id int, name STRING, phone STRING, address STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
STORED AS TEXTFILE;
And then imported the csv file in the sample table. and it was successfully imported.
then i tried using.
insert into encode_test select * from sample;
But i am getting this new error
Permission denied: user=root, access=WRITE, inode="/user":h dfs:supergroup:drwxr-xr-x
at org.apache.hadoop.hdfs.server.namenode.DefaultAuthorizationProvider.c heckFsPermission(DefaultAuthorizationProvider.java:279)
I'n new into hadoop
Please refer to this link from where i tried this problem
In Hive DDL, ROW FORMAT SERDE and FIELDS TERMINATED BY cannot co-exist together. Instead you can use, field.delim serde property.
create table encode_test(id int, name STRING, phone STRING, address STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES (
'field.delim'=',',
'column.encode.columns'='phone,address',
'column.encode.classname'='org.apache.hadoop.hive.serde2.Base64WriteOnly')
STORED AS TEXTFILE;
And for the PermissionDenied exception, run the hive queries as either hdfs or hive user since root user does not have WRITE access to HDFS.
I have created a table in Hive with the following query:
create table if not exists employee(CASE_NUMBER String,
CASE_STATUS String,
CASE_RECEIVED_DATE DATE,
DECISION_DATE DATE,
EMPLOYER_NAME STRING,
PREVAILING_WAGE_PER_YEAR BIGINT,
PAID_WAGE_PER_YEAR BIGINT,
order_n int) partitioned by (JOB_TITLE_SUBGROUP STRING) row format delimited fields terminated by ',';
I tried loading data into the create table using below query:
LOAD DATA INPATH '/salary_data.csv' overwrite into table employee partition (JOB_TITLE_SUBGROUP);
For the partitioned table, I have even set following configuration :
set hive.exec.dynamic.partition.mode=nonstrict;
set hive.exec.dynamic.partition=true;
But I am getting below error while executing the load query:
Your query has the following error(s):
Error while compiling statement: FAILED: SemanticException org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:Invalid partition key & values; keys [job_title_subgroup, ], values [])
Please help.
If you want to load data into a Hive partition, you have to provide the value of the partition itself in the LOAD DATA query. So in this case, your query would be something like this.
LOAD DATA INPATH '/salary_data.csv' overwrite into table employee partition (JOB_TITLE_SUBGROUP="Value");
Where "Value" is the name of the partition in which you are loading your data. The reason is because Hive will use "Value" to create the directory in which your .csv is going to be stored, which will be something like this: .../employee/JOB_TITLE_SUBGROUP=Value. I hope this helps.
Check the documentation for details on the LOAD DATA syntax.
EDITED
Since the table has dynamic partition, one solution would be loading the .csv into an external table (e.g. employee_external) and then execute an INSERT command like this:
INSERT OVERWRITE INTO TABLE employee PARTITION(JOB_TITLE_SUBGROUP)
SELECT CASE_NUMBER, CASE_STATUS, (...), JOB_TITLE_SUBGROUP
FROM employee_external
I might be little late to reply but can try below steps:
Set below properties first :
Ø set hive.exec.dynamic.partition.mode=nonstrict;
Ø set hive.exec.dynamic.partition=true;
Create temp table first:
CREATE EXTERNAL TABLE IF NOT EXISTS employee_temp(
ID STRING,
Name STRING,
Salary STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
tblproperties ("skip.header.line.count"="1");
Load Data in temporary table:
hive> LOAD DATA INPATH 'filepath/employee.csv' OVERWRITE INTO TABLE employee;
Create Partitioned Table:
CREATE EXTERNAL TABLE IF NOT EXISTS employee_part(
ID STRING,
Name STRING)
PARTITIONED BY (Salary STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
tblproperties ("skip.header.line.count"="1");
Load Data into partitioned table from intermediate / temp table:
INSERT OVERWRITE TABLE employee_part PARTITION (SALARY) SELECT * FROM employee;
I am creating an ORC file in Java. For each row I want the fields to be comma delimited. Here is my java code:
ObjectInspector inspector = ObjectInspectorFactory.getReflectionObjectInspector(String.class,ObjectInspectorFactory.ObjectInspectorOptions.JAVA);
this.mWriter = OrcFile.createWriter(fs, fsPath, config, inspector, stripSize, CompressionKind.ZLIB, bufferSize,0);
this.mWriter.addRow(new Text("shekhar,saha"));
this.mWriter.addRow(new Text("ram,shyam"));
this.mWriter.addRow(new Text("jhon,cena"));
this.mWriter.close();
Is this right way of creating it?
I am trying to load data in a Hive table. This is how I have created my table:
create table demo ( name1 STRING,name2 STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS ORC tblproperties ("orc.compress"="ZLIB");
But I am not able to load data. When I am reading data from the table its throwing class caste exception org.apache.hadoop.hive.ql.io.orc.OrcStruct cannot be cast to org.apache.hadoop.io.Text
I am trying to load data into hive tables which is delimited by double pipe(||). When I try this :
Sample I/P:
1405983600000||111.111.82.41||806065581||session-id
Creating table in hive:
create table test_hive(k1 string, k2 string, k3 string, k4 string,) row format delimited fields terminated by '||' stored as textfile;
Loading data from text file:
load data local inpath '/Desktop/input.txt' into table test_hive;
When I do this it is storing data in the below format:
1405983600000 tabspace-as-second-column 111.111.82.41 tabspace-as-fourth-column
Where as I am expecting the data in table to be
1405983600000 111.111.82.41 806065581 session-id
Kindly help me out I have tried different options on this but unable to resolve it
Multicharater delimiter eg. || is not supported in Hive till ver 0.13 . So fields terminated by || won't work out.There is an alter native for this.
CREATE EXTERNAL TABLE page_view(viewTime INT, userid BIGINT,
page_url STRING, referrer_url STRING,
ip STRING COMMENT 'IP Address of the User',
country STRING COMMENT 'country of origination')
COMMENT 'This is the staging page view table'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\054'
SERDE serde_name WITH SERDEPROPERTIES (field.delim='||')
STORED AS TEXTFILE
LOCATION '<hdfs_location>';
The default serde can be used. Multi character delimiters can be used for fields , line , escape characters by specifying them in the serde properties.
This issue has been resolved in hive 14 with the use of multidelimiter serde. Please find documentation here.
https://cwiki.apache.org/confluence/display/Hive/MultiDelimitSerDe
You could do this if you don't want to use alternate serde or have earlier version of hive:
create external table my_table (line string) location /path/file;
Then create view on top:
create view my_view as select split(line,'\\|\\|')[0] as column_1
, split(line,'\\|\\|')[1] as column_2
, split(line,'\\|\\|')[2] as column_3
from my_table;
Query the view. Good luck.