I have an external table with JSON data and I am using JsonSerde to populate data into the table. I am properly getting the data populated and when I query the data I am able to see the results correctly.
But,when I use desc command on that table I am getting from deserializer text for all the column comments.
Below is the table creation ddl.
CREATE EXTERNAL TABLE IF NOT EXISTS my_table (
field1 string COMMENT 'This is a field1',
field2 int COMMENT 'This is a field2',
field3 string COMMENT 'This is a field3',
field4 double COMMENT 'This is a field4'
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.JsonSerde'
Location '/user/uszszb6/json_test/data';
Entries in the data file.
{"field1":"data1","field2":100,"field3":"more data1","field4":123.001}
{"field1":"data2","field2":200,"field3":"more data2","field4":123.002}
{"field1":"data3","field2":300,"field3":"more data3","field4":123.003}
{"field1":"data4","field2":400,"field3":"more data4","field4":123.004}
When I use use the command desc my_table, I get the below output.
+-----------+------------+--------------------+--+
| col_name | data_type | comment |
+-----------+------------+--------------------+--+
| field1 | string | from deserializer |
| field2 | int | from deserializer |
| field3 | string | from deserializer |
| field4 | double | from deserializer |
+-----------+------------+--------------------+--+
JsonSerde is not able to capture the comments properly. I have also tried with other JSONSerde like
org.openx.data.jsonserde.JsonSerDe
org.apache.hive.hcatalog.data.JsonSerDe
com.amazon.elasticmapreduce.JsonSerde
But desc command output is same. There is a JIRA ticket for this bug [https://issues.apache.org/jira/browse/HIVE-6681][1]
According to ticket it's resolved in version 0.13, I am using hive 1.2.1 but still I am facing this issue.
Could anyone share your thoughts on resolving this issue.
Yeah, it looks like it's an hive bug that affects all the Json SerDes, but have you tried using DESCRIBE EXTENDED ?
DESCRIBE EXTENDED my_table;
hive> describe extended json_serde_test;
OK
browser string from deserializer
device_uuid string from deserializer
custom struct<customer_id:string> from deserializer
Detailed Table Information
Table(tableName:json_serde_test,dbName:default, owner:rcongiu,
createTime:1448477902, lastAccessTime:0, retention:0,
sd:StorageDescriptor(cols:[FieldSchema(name:browser, type:string,
comment:hello), FieldSchema(name:device_uuid, type:string, comment:my
name is elder price), FieldSchema(name:custom,
type:struct<customer_id:string>, comment:null)],
location:hdfs://localhost:9000/user/hive/warehouse/json_serde_test,
inputFormat:org.apache.hadoop.mapred.TextInputFormat,
outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat,
compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null,
serializationLib:org.openx.data.jsonserde.JsonSerDe, parameters:
{serialization.format=1, mapping.customer_id=Customer ID}),
bucketCols:[], sortCols:[], parameters:{},
skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[],
skewedColValueLocationMaps:{}), storedAsSubDirectories:false),
partitionKeys:[], parameters:{numFiles=1,
transient_lastDdlTime=1448477903, COLUMN_STATS_ACCURATE=true,
totalSize=128, numRows=0, rawDataSize=0}, viewOriginalText:null,
viewExpandedText:null, tableType:MANAGED_TABLE)
Time taken: 0.073 seconds, Fetched: 5 row(s)
Will output a json-ish detailed description that includes comments..kind of hard to read but it is showing me the comments and may be enough for your purposes..or not.
Related
I have a hive table with the following structure and data:
Table structure:
CREATE EXTERNAL TABLE IF NOT EXISTS db_crprcdtl.shcar_dtls
ID string,
CSK string,
BRND string,
MKTCP string,
AMTCMP string,
AMTSP string,
RLBRND string,
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LINES TERMINATED BY '\n'
STORED AS TEXTFILE
LOCATION '/on/hadoop/dir/'
-------------------------------------------------------------------------------
ID | CSK | BRND | MKTCP | AMTCMP
-------------------------------------------------------------------------------
782 flatn,grpl,mrtn hnd,mrc,nsn 34555,56566,66455 38900,59484,71450
1231 jikl,bngr su,mrc,frd 56566,32333,45000 59872,35673,48933
123 unsrvl tyt,frd,vlv 25000,34789,33443 29892,38922,36781
Trying to push this data into the SQL Server. But while doing so, getting the following error message:
SQL Error [107090] [S0001]: HdfsBridge::recordReaderFillBuffer - Unexpected error encountered filling record reader buffer: HadoopExecutionException: Not enough columns in this line.
What I tried:
There's an online article where the author has documented similar kind of issues. I tried to implement one of them Looked in Excel and found two columns that had carriage returns but this also doesn't come handy.
Any suggestion/help would be really appreciated. Thanks
If I'm able to understand your issue, then it seems that your , separated data is getting divided into various columns rather one column on the SQL-SERVER, something like:
------------------------------
ID |CSK |BRND |MKTCP |AMTCMP
------------------------------
782 flatn grpl mrtn hnd mrc nsn 345 56566 66455 38900 59484 71450
1231 jikl bngr su mrc frd 56566 32333 45000 59872 35673 48933
123 unsrvl tyt frd vlv 25000 34789 33443 29892 38922 36781
So, if you look on Hive there are only 5 columns. While on SQL-SERVER the same. This I presume as you haven't shared the schema. But if that's the case, then you see that there are more than 5 values are being passed. While the schema definition is only of 5 columns.
So the error is populating.
Refer this Document by MS and try to create a FILE_FORMAT with FIELD_TERMINATOR ='\t',
like:
CREATE EXTERNAL FILE FORMAT <name>
WITH (
FORMAT_TYPE = DELIMITEDTEXT,
FORMAT_OPTIONS (
FIELD_TERMINATOR ='\t',
| STRING_DELIMITER = string_delimiter
| First_Row = integer -- ONLY AVAILABLE SQL DW
| DATE_FORMAT = datetime_format
| USE_TYPE_DEFAULT = { TRUE | FALSE }
| Encoding = {'UTF8' | 'UTF16'} )
);
Hope that helps to resolve to your issue :)
I have a basic Athena query like this:
SELECT *
FROM my.dataset LIMIT 10
When I try to run it I get an error message like this:
Your query has the following error(s):
HIVE_BAD_DATA: Error parsing field value for field 2: For input string: "32700.000000000004"
How do I identify the S3 document that has the invalid field?
My documents are JSON.
My table looks like this:
CREATE EXTERNAL TABLE my.data (
`id` string,
`timestamp` string,
`profile` struct<
`name`: string,
`score`: int>
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES (
'serialization.format' = '1',
'ignore.malformed.json' = 'true'
)
LOCATION 's3://my-bucket-of-data'
TBLPROPERTIES ('has_encrypted_data'='false');
Inconsistent schema
Inconsistent schema is when values in some rows are of different data type. Let's assume that we have two json files
// inside s3://path/to/bad.json
{"name":"1Patrick", "age":35}
{"name":"1Carlos", "age":"eleven"}
{"name":"1Fabiana", "age":22}
// inside s3://path/to/good.json
{"name":"2Patrick", "age":35}
{"name":"2Carlos", "age":11}
{"name":"2Fabiana", "age":22}
Then a simple query SELECT * FROM some_table will fail with
HIVE_BAD_DATA: Error parsing field value 'eleven' for field 1: For input string: "eleven"
However, we can exclude that file within WHERE clause
SELECT
"$PATH" AS "source_s3_file",
*
FROM some_table
WHERE "$PATH" != 's3://path/to/bad.json'
Result:
source_s3_file | name | age
---------------------------------------
s3://path/to/good.json | 1Patrick | 35
s3://path/to/good.json | 1Carlos | 11
s3://path/to/good.json | 1Fabiana | 22
Of course, this is the best case scenario when we know which files are bad. However, you can employ this approach to somewhat manually infer which files are good. You can also use LIKE or regexp_like to walk through multiple files at a time.
SELECT
COUNT(*)
FROM some_table
WHERE regexp_like("$PATH", 's3://path/to/go[a-z]*.json')
-- If this query doesn't fail, that those files are good.
The obvious drawback of such approach is cost to execute query and time spent, especially if it is done file by file.
Malformed records
In the eyes of AWS Athena, good records are those which are formatted as a single JSON per line:
{ "id" : 50, "name":"John" }
{ "id" : 51, "name":"Jane" }
{ "id" : 53, "name":"Jill" }
AWS Athena supports OpenX JSON SerDe library which can be set to evaluate malformed records as NULL by specifying
-- When you create table
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ( 'ignore.malformed.json' = 'true')
when you create table. Thus, the following query will reveal files with malformed records:
SELECT
DISTINCT("$PATH")
FROM "some_database"."some_table"
WHERE(
col_1 IS NULL AND
col_2 IS NULL AND
col_3 IS NULL
-- etc
)
Note: you can use only a single col_1 IS NULL if you are 100% sure that it doesn't contain empty fields other then in corrupted rows.
In general, malformed records are not that big of a deal provided that 'ignore.malformed.json' = 'true'. For example the following query will still succeed
For example if a file contains:
{"name": "2Patrick","age": 35,"address": "North Street"}
{
"name": "2Carlos",
"age": 11,
"address": "Flowers Street"
}
{"name": "2Fabiana","age": 22,"address": "Main Street"}
the following query will still succeed
SELECT
"$PATH" AS "source_s3_file",
*
FROM some_table
Result:
source_s3_file | name | age | address
-----------------------------|----------|-----|-------------
1 s3://path/to/malformed.json| 2Patrick | 35 | North Street
2 s3://path/to/malformed.json| | |
3 s3://path/to/malformed.json| | |
4 s3://path/to/malformed.json| | |
5 s3://path/to/malformed.json| | |
6 s3://path/to/malformed.json| | |
7 s3://path/to/malformed.json| 2Fabiana | 22 | Main Street
While with 'ignore.malformed.json' = 'false' (which is the default behaviour) exactly the same query will throw an error
HIVE_CURSOR_ERROR: Row is not a valid JSON Object - JSONException: A JSONObject text must end with '}' at 2 [character 3 line 1]
*outdated Oracle version
I have a table for receipt data.
I want to get some data from field EXT_ATTR. such as PAYMENT_RECEIPT_NO
The field "EXT_ATTR" is varchar(4000) stored JSON value
SerialId | EXT_ATTR
1 |
{
"PAYMENT_RECEIPT_NO": "PS00000000000000001",
"IS_CORPOR": "1",
"POSTCODE1": "51000",
"POSTCODE2": "51000",
"BILLADDR1PART1": "BILLADDR1PART1_DATA",
"BILLADDR1PART2": "BILLADDR1PART2_DATA",
"NEED_PRINT_WHT": "1",
"WHT_AMT": "0",
"TRXAMT": "2340600",
"LOCATIONID": "02140",
"PAYMENT_METHOD_NAME": "Cash",
"WITH_TAX": "1"
}
2 |
{
"PAYMENT_RECEIPT_NO": "PS00000000000000055",
"IS_CORPOR": "1",
"POSTCODE1": "51000",
"POSTCODE2": "51000",
"BILLADDR1PART1": "BILLADDR1PART1_DATA",
"BILLADDR1PART2": "BILLADDR1PART2_DATA",
"NEED_PRINT_WHT": "1",
"WHT_AMT": "0",
"TRXAMT": "2340600",
"LOCATIONID": "02140",
"PAYMENT_METHOD_NAME": "Cash",
"WITH_TAX": "1"
}
How can I extract varchar filed to get only value.
SerialId | PAYMENT_RECEIPT_NO
1 | PS00000000000000001
2 | PS00000000000000055
Thank you very much.
to work with json documents you can use PL/JSON
if you want to parse it without json Tools, than you can use substr, instr function in Oracle.
depending on what your string looks like, you have to adjust string positions.
create table tab (json varchar2(1000));
insert into tab values('{"PAYMENT_RECEIPT_NO": "PS00000000000000001","IS_CORPOR": "1","POSTCODE1": "51000","POSTCODE2": "51000","BILLADDR1PART1": "BILLADDR1PART1_DATA","BILLADDR1PART2": "BILLADDR1PART2_DATA","NEED_PRINT_WHT": "1","WHT_AMT": "0","TRXAMT": "2340600","LOCATIONID": "02140","PAYMENT_METHOD_NAME": "Cash","WITH_TAX": "1"}');
insert into tab values('{"PAYMENT_RECEIPT_NO": "PS00000000000000055","IS_CORPOR": "1","POSTCODE1": "51000","POSTCODE2": "51000","BILLADDR1PART1": "BILLADDR1PART1_DATA","BILLADDR1PART2": "BILLADDR1PART2_DATA","NEED_PRINT_WHT": "1","WHT_AMT": "0","TRXAMT": "2340600","LOCATIONID": "02140","PAYMENT_METHOD_NAME": "Cash","WITH_TAX": "1"}');
select substr(json,instr(json,': ',1,1)+3,instr(json,',',1,1)-instr(json,': ',1,1)-4)
from tab;
| SUBSTR(JSON,INSTR(JSON,':',1,1)+3,INSTR(JSON,',',1,1)-INSTR(JSON,':',1,1)-4) |
| :--------------------------------------------------------------------------- |
| PS00000000000000001 |
| PS00000000000000055 |
db<>fiddle here
JSON functions are defined for Database Oracle12c+ version. APEX_JSON package with release 5.0+ should be installed for the previous releases. Whenever installation complete, then the following code might be used as an XML data type manner through APEX_JSON.TO_XMLTYPE() function in order to extract the desired values :
WITH t AS
(
SELECT SerialId, APEX_JSON.TO_XMLTYPE(Payment_Receipt_No) AS xml_data
FROM tab
)
SELECT SerialId, Payment_Receipt_No
FROM t
CROSS JOIN
XMLTABLE('/json'
PASSING xml_data
COLUMNS
Payment_Receipt_No VARCHAR2(100) PATH 'PAYMENT_RECEIPT_NO'
)
I have a table like following Qubole:
use dm;
CREATE EXTERNAL TABLE IF NOT EXISTS fact (
id string,
fact_attr struct<
attr1 : String,
attr2 : String
>
)
STORED AS PARQUET
LOCATION 's3://my-bucket/DM/fact'
I have created parallel table in Snowflake like following:
CREATE TABLE IF NOT EXISTS dm.fact (
id string,
fact_attr variant
)
My ETL process loads the data into qubole table like:
+------------+--------------------------------+
| id | fact_attr |
+------------+--------------------------------+
| 1 | {"attr1": "a1", "attr2": "a2"} |
| 2 | {"attr1": "a3", "attr2": null} |
+------------+--------------------------------+
I am trying to sync this data to snowflake using Merge command, like
MERGE INTO DM.FACT dst USING %s src
ON dst.id = src.id
WHEN MATCHED THEN UPDATE SET
fact_attr = parse_json(src.fact_attr)
WHEN NOT MATCHED THEN INSERT (
id,
fact_attr
) VALUES (
src.id,
parse_json(src.fact_attr)
);
I am using PySpark to sync the data:
df.write \
.option("sfWarehouse", sf_warehouse) \
.option("sfDatabase", sf_database) \
.option("sfSchema", sf_schema) \
.option("postactions", query) \
.mode("overwrite") \
.snowflake("snowflake", sf_warehouse, sf_temp_table)
With above command I am getting following error:
pyspark.sql.utils.IllegalArgumentException: u"Don't know how to save StructField(fact_attr,StructType(StructField(attr1,StringType,true), StructField(attr2,StringType,true)),true) of type attributes to Snowflake"
I have read through following links but no success:
Semi-structured Data Types
Querying Semi-structured Data
Question:
How can I insert/sync data from Qubole Hive table which has STRUCT field to snowflake?
The version of your Spark Connector for Snowflake in use at the time of trying this lacked support for variant data types.
Support was introduced in their connector version 2.4.4 (released July 2018) onwards, where the StructType fields are now auto-mapped to a VARIANT data type that will work with your MERGE command.
I have gz files in a folder. I need only 3 columns from these files, but each line has over 100 of them. At the moment I create a view this way.
drop table MAK_CHARGE_RCR;
create external table MAK_CHARGE_RCR
(LINE string)
STORED as SEQUENCEFILE
LOCATION '/apps/hive/warehouse/mydb.db/file_rcr';
drop view VW_MAK_CHARGE_RCR;
create view VW_MAK_CHARGE_RCR as
Select LINE[57] as CREATE_DATE, LINE[64] as SUBS_KEY, LINE[63] as RC_TERM_NAME
from
(Select split(LINE, '\\|') as LINE
from MAK_CHARGE_RCR) a;
The view has the fields I need. Now I have to do the same, but without CTAS and I am not sure how to go about it. What can I do?
I was told the table must look like this
create external table MAK_CHARGE_RCR
(CREATE_DATE string, SUBS_KEY string, RC_TERM_NAME etc)
I could split the line like this
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\\|'
but I'll need to list every column. I have another group of files with over 1000 columns. All of them I'll need to list. This just seems a bit excessive, so I wondered if it is possible to do
create external table arstel.MAK_CHARGE_RCR
(split(LINE, '\\|')[57] string,
split(LINE, '\\|')[64] string
etc)
This doesn't work obviously, but maybe there are work arounds?
RegexSerDe
For educational purposes
P.s.
I intend to create an enhanced version of the CSV SerDe that excepts an additional parameter with the positions of the requested columns.
Demo
bash
echo {a..c}{1..100} | xargs -n 100 | tr ' ' '|' | \
hdfs dfs -put - /user/hive/warehouse/mytable/data.txt
hive
create external table mytable
(
col58 string
,col64 string
,col65 string
)
row format serde 'org.apache.hadoop.hive.serde2.RegexSerDe'
with serdeproperties ("input.regex" = "^(?:([^|]*)\\|){58}(?:([^|]*)\\|){6}([^|]*)\\|.*$")
stored as textfile
location '/user/hive/warehouse/mytable'
;
select * from mytable
;
+---------------+---------------+---------------+
| mytable.col58 | mytable.col64 | mytable.col65 |
+---------------+---------------+---------------+
| a58 | a64 | a65 |
| b58 | b64 | b65 |
| c58 | c64 | c65 |
+---------------+---------------+---------------+