So I'm trying to run the following simple query on redshift spectrum:
select * from company.vehicles where vehicle_id is not null
and it return 0 rows(all of the rows in the table are null). However when I run the same query on athena it works fine and return results. Tried msck repair but both athena and redshift are using the same metastore so it shouldn't matter.
I also don't see any errors.
The format of the files is orc.
The create table query is:
CREATE EXTERNAL TABLE 'vehicles'(
'vehicle_id' bigint,
'parent_id' bigint,
'client_id' bigint,
'assets_group' int,
'drivers_group' int)
PARTITIONED BY (
'dt' string,
'datacenter' string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
LOCATION
's3://company-rt-data/metadata/out/vehicles/'
TBLPROPERTIES (
'CrawlerSchemaDeserializerVersion'='1.0',
'CrawlerSchemaSerializerVersion'='1.0',
'classification'='orc',
'compressionType'='none')
Any idea?
How did you create your external table ??
For Spectrum,you have to explicitly set the parameters to treat what should be treated as null
add the parameter 'serialization.null.format'='' in TABLE PROPERTIES so that all columns with '' will be treated as NULL to your external table in spectrum
**
CREATE EXTERNAL TABLE external_schema.your_table_name(
)
row format delimited
fields terminated by ','
stored as textfile
LOCATION [filelocation]
TABLE PROPERTIES('numRows'='100', 'skip.header.line.count'='1','serialization.null.format'='');
**
Alternatively,you can setup the SERDE-PROPERTIES while creating the external table which will automatically recognize NULL values
Eventually it turned out to be a bug in redshift. In order to fix it, we needed to run the following command:
ALTER TABLE table_name SET TABLE properties(‘orc.schema.resolution’=‘position’);
I had a similar problem and found this solution.
In my case I had external tables that were created with Athena pointing to an S3 bucket that contained heavily nested JSON data. To access them with Redshift I used json_serialization_enable to true; before my queries to make the nested JSON columns queryable. This lead to some columns being NULL when the JSON exceeded a size limit, see here:
If the serialization overflows the maximum VARCHAR size of 65535, the cell is set to NULL.
To solve this issue I used Amazon Redshift Spectrum instead of serialization: https://docs.aws.amazon.com/redshift/latest/dg/tutorial-query-nested-data.html.
Related
I have some data stored in GCS bucket in the following path:
gcs://my-bucket/my_data/subfolder1/subfolder2/**.csv.gz
I intent to create an external table mapping to my_data and want the external table is able to partition the data by different level of subfolders. Note that subfolder1 or subfolder2 don't have a hive partition prefix, i.e, not in the format of prefix=value.
If I would write some pseudo code in Athena syntax, it would be something like below:
CREATE EXTERNAL TABLE `my_data`(
--Column specs go here---
)
PARTITIONED BY (
`partition_0` string,
`partition_1` string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS INPUTFORMAT
'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
'gcs://my-bucket/my-data/'
TBLPROPERTIES (...)
As a result of the pseudo code, the table will consists of two partition columns in addition to columns defined in the column spec.
partition_0
partition_1
Queries filtering on these two columns will then benefits from partition pruning.
Would anyone please advise if this possible in BigQuery. If yes, how I should go about it in SQL?
I have created a table using partition. I tried two ways for my s3 bucket folder as following but both ways I get no records found when I query with where clause containing partition clause.
My S3 bucket looks like following. part*.csv is what I want to query in Athena. There are other folders at same location along side output, within output.
s3://bucket-rootname/ABC-CASE/report/f78dea49-2c3a-481b-a1eb-5169d2a97747/output/part-filename121231.csv
s3://bucket-rootname/XYZ-CASE/report/678d1234-2c3a-481b-a1eb-5169d2a97747/output/part-filename213123.csv
my table looks like following
Version 1:
CREATE EXTERNAL TABLE `mytable_trial1`(
`status` string,
`ref` string)
PARTITIONED BY (
`casename` string,
`id` string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION
's3://bucket-rootname/'
TBLPROPERTIES (
'has_encrypted_data'='false',
'skip.header.line.count'='1')
ALTER TABLE mytable_trial1 add partition (casename="ABC-CASE",id="f78dea49-2c3a-481b-a1eb-5169d2a97747") location "s3://bucket-rootname/casename=ABC-CASE/report/id=f78dea49-2c3a-481b-a1eb-5169d2a97747/output/";
select * from mytable_trial1 where casename='ABC-CASE' and report='report' and id='f78dea49-2c3a-481b-a1eb-5169d2a97747' and foldername='output';
Version 2:
CREATE EXTERNAL TABLE `mytable_trial1`(
`status` string,
`ref` string)
PARTITIONED BY (
`casename` string,
`report` string,
`id` string,
`foldername` string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION
's3://bucket-rootname/'
TBLPROPERTIES (
'has_encrypted_data'='false',
'skip.header.line.count'='1')
ALTER TABLE mytable_trial1 add partition (casename="ABC-CASE",report="report",id="f78dea49-2c3a-481b-a1eb-5169d2a97747",foldername="output") location "s3://bucket-rootname/casename=ABC-CASE/report=report/id=f78dea49-2c3a-481b-a1eb-5169d2a97747/foldername=output/";
select * from mytable_trial1 where casename='ABC-CASE' and id='f78dea49-2c3a-481b-a1eb-5169d2a97747'
Show partitions shows this partition but no records found with where clause.
I worked with the AWS Support and we were able to narrow down the issue. Version 2 was right one to use since it has four partitions like my S3 bucket. Also, the Alter table command had issue with location. I used hive format location which was incorrect since my actual S3 location is not hive format. So correcting the command to following worked for me.
ALTER TABLE mytable_trial1 add partition (casename="ABC-CASE",report="report",id="f78dea49-2c3a-481b-a1eb-5169d2a97747",foldername="output") location "s3://bucket-rootname/ABC-CASE/report/f78dea49-2c3a-481b-a1eb-5169d2a97747/output/";
Preview table now shows my entries.
When you create a ORC table in hive, you are changing the file type to be orc. This means you can't look at a specific file outside of the orc table.
Here's an example orc create table statement
CREATE TABLE IF NOT EXISTS table_orc_v1
(
col1 int,
col2 int
)
PARTITIONED BY (odate date)
CLUSTERED BY (col1) INTO 10 BUCKETS
STORED AS ORC TBLPROPERTIES('transactional'='true');
If I try to make this a csv table (like you do on a non-orc table) will it
1) not affect table performance
2) slow down performance as it converts things to a csv file that you can never read
3) give me some benefit that I'm not aware of
4) do something else
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
if you are using any binary format (ORC, AVRO, Parquet) to store you data then ROW FORMAT DELIMITED FIELDS TERMINATED BY is just ignored, you can use it in your table syntax, it might not give you any error. However they are not being used
I'm trying to create a bucket in hive by using following commands:
hive> create table emp( id int, name string, country string)
clustered by( country)
row format delimited
fields terminated by ','
stored as textfile ;
Command is executing successfully: when I load data into this table, it executes successfully and all data is shown when using select * from emp.
However, on HDFS it is only creating one table and only one file is there with all data. That is, there is no folder for specific country records.
First of all, in the DDL statement you have to explicitly mention how many buckets you want.
create table emp( id int, name string, country string)
clustered by( country)
INTO 2 BUCKETS
row format delimited
fields terminated by ','
stored as textfile ;
In the above statement I have mention 2 buckets, similarly you can mention any number you want.
Still you are not done!!
After that, while loading data into the table you also have to mention the below hint to hive.
set hive.enforce.bucketing = true;
That should do it.
After this you should be able to see that number of files created under the table directory is same as the number of buckets mentioned in the DDL statement.
Bucketing doesn't create HDFS folders, rather if you want a separate floder to be created for a country then you should PARTITION.
Please go through hive partitioning and bucketing in detail.
I have data stored in S3 like:
/bucket/date=20140701/file1
/bucket/date=20140701/file2
...
/bucket/date=20140701/fileN
/bucket/date=20140702/file1
/bucket/date=20140702/file2
...
/bucket/date=20140702/fileN
...
My understanding is that if I pull in that data via Hive, it will automatically interpret date as a partition. My table creation looks like:
CREATE EXTERNAL TABLE search_input(
col 1 STRING,
col 2 STRING,
...
)
PARTITIONED BY(date STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LINES TERMINATED BY '\n'
LOCATION 's3n://bucket/';
However Hive doesn't recognize any data. Any queries I run return with 0 results. If I instead just grab one of the dates via:
CREATE EXTERNAL TABLE search_input_20140701(
col 1 STRING,
col 2 STRING,
...
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
LINES TERMINATED BY '\n'
LOCATION 's3n://bucket/date=20140701';
I can query data just fine.
Why doesn't Hive recognize the nested directories with the "date=date_str" partition?
Is there a better way to have Hive run a query over multiple sub-directories and slice it based on a datetime string?
In order to get this to work I had to do 2 things:
Enable recursive directory support:
SET mapred.input.dir.recursive=true;
SET hive.mapred.supports.subdirectories=true;
For some reason it would still not recognize my partitions so I had to recover them via:
ALTER TABLE search_input RECOVER PARTITIONS;
You can use:
SHOW PARTITIONS table;
to check and see that they've been recovered.
I had faced the same issue and realized that hive does not have partitions metadata with it. So we need to add that metadata using ALTER TABLE ADD PARTITION query. It becomes tedious, if you have few hundred partitions to create same queries with different values.
ALTER TABLE <table name> ADD PARTITION(<partitioned column name>=<partition value>);
Once you run above query for all available partitions. You should see the results in hive queries.