orc | Apache ORC - the smallest , fastest columnar storage
kandi X-RAY | orc Summary
kandi X-RAY | orc Summary
This project includes both a Java library and a C++ library for reading and writing the Optimized Row Columnar (ORC) file format. The C++ and Java libraries are completely independent of each other and will each read all versions of ORC files. But the C++ library only writes the original (Hive 0.11) version of ORC files, and will be extended in the future.
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orc Examples and Code Snippets
@Override
public String toString() {
return "The orc blacksmith";
}
Community Discussions
Trending Discussions on orc
QUESTION
I am currently on the path of learning C++ and this is an example program I wrote for the course I'm taking. I know that there are things in here that probably makes your skin crawl if you're experienced in C/C++, heck the program isn't even finished, but I mainly need to know why I keep receiving this error after I enter my name: Exception thrown at 0x79FE395E (vcruntime140d.dll) in Learn.exe: 0xC0000005: Access violation reading location 0xCCCCCCCC.
I know there is something wrong with the constructors and initializations of the member variables of the classes but I cannot pinpoint the problem, even with the debugger. I am running this in Visual Studio and it does initially run, but I realized it does not compile with GCC. Feel free to leave some code suggestions, but my main goal is to figure out the program-breaking issue.
ANSWER
Answered 2021-Jun-13 at 00:59The problem is here:
QUESTION
The method plant()
takes a String
and a 2D array of String[][]
as its inputs. The strings within the array should not be replaced by the inputted word.
ANSWER
Answered 2021-Jun-03 at 10:30This should help you:
QUESTION
I'm try to execute the below hive query on Azure HDInsight cluster but it's taking unprecedented amount of time to finish. Did implemented hive settings but of no use. Below are the details:
Table
ANSWER
Answered 2021-Jun-07 at 03:19if you don't have index on your fk columns , you should add them for sure , here is my suggestion:
QUESTION
Could you please help me for below formula little bit complicated Problem is In a sheet I have three column A,B,C any one column amount if it is same in D column need to highlight and show which column A or B orC.. Example
...ANSWER
Answered 2021-May-28 at 13:57XLOOKUP
unlike VLOOKUP
returns a reference to the cell and not just the value of the cell.
With this in mind =XLOOKUP(D2,A2:C2,A2:C2,NA())
will return the value if it exists as well as the reference.
If we wrap the Return Array with the Column
function it will return the column number.
=XLOOKUP(D2,A2:C2,COLUMN(A2:C2),NA())
Add the ADDRESS
function to return the cell address (this will return the address on row 1)
=XLOOKUP(D2,A2:C2,ADDRESS(1,COLUMN(A2:C2),4),NA())
Now substitute the 1 in the cell address with a blank:
=SUBSTITUTE(XLOOKUP(D2,A2:C2,ADDRESS(1,COLUMN(A2:C2),4),NA()),"1","")
QUESTION
Could someone tell me how can I reduce the nested for loops and if conditions from the below python code, so that it will become less complex. As of now, I am unable to break this code further, hence need help.
...ANSWER
Answered 2021-May-17 at 15:05Please consider sorting sequences and joining them with itertools.groupby()
or just a generator:
QUESTION
I want to create a BigQuery partitioned table by mydate
column from partitioned ORC.
Files in GCS :
...ANSWER
Answered 2021-May-10 at 11:56I think we can do this by Providing a custom partition key schema encoded via the source_uri_prefix field.
Using below links and examples [1] & [2] related to Partition Schema detection modes, I think you can do it. [1] https://cloud.google.com/bigquery/docs/hive-partitioned-loads-gcs#command-line-tool [2] https://cloud.google.com/bigquery/docs/hive-partitioned-loads-gcs
QUESTION
I have a table with 3 partition columns
...ANSWER
Answered 2021-Apr-20 at 10:28Setting this to false helped.
QUESTION
I'm trying to read a zst-compressed file using Spark on Scala.
...ANSWER
Answered 2021-Apr-18 at 21:25Since I didn't want to build Hadoop by myself, inspired by the workaround used here, I've configured Spark to use Hadoop native libraries:
QUESTION
We are testing our Hadoop applications as part of migrating from Hortonworks Data Platform (HDP v3.x) to Cloudera Data Platform (CDP) version 7.1. While testing, we found below issue while trying to create Managed Hive Table. Please advise on possible solutions. Thank you!
Error: Error while compiling statement: FAILED: Execution Error, return code 40000 from org.apache.hadoop.hive.ql.ddl.DDLTask. MetaException(message:A managed table's location should be located within managed warehouse root directory or within its database's managedLocationUri. Table MANAGED_TBL_A's location is not valid:hdfs://cluster/prj/Warehouse/Secure/APP/managed_tbl_a, managed warehouse:hdfs://cluster/warehouse/tablespace/managed/hive) (state=08S01,code=40000)
DDL Script
...ANSWER
Answered 2021-Apr-13 at 11:18hive.metastore.warehouse.dir
- is a warehouse root directory.
When you create the database, specify MANAGEDLOCATION
- a location root for managed tables and LOCATION
- root for external tables.
MANAGEDLOCATION
is within hive.metastore.warehouse.dir
Setting the metastore.warehouse.tenant.colocation
property to true
allows a common location for managed tables (MANAGEDLOCATION) outside the warehouse root directory, providing a tenant-based common root for setting quotas and other policies.
See more details in this manual: Hive managed location.
QUESTION
I tried to submit my application and change the coalese[k]
in my code by different combinations:
Firstly, I read some data from my local disk:
...ANSWER
Answered 2021-Apr-12 at 18:13Spark can read a csv file only with one executor as there is only a single file.
Compared to files which are located in a distributed files system such as HDFS where a single file can be stored in multiple partitions. That means your resulting Dataframe df
has only a single partition. You can check that using df.rdd.getNumPartitions
. See also my answer on How is a Spark Dataframe partitioned by default?
Note that coalesce
will collapse partitions on the same worker, so calling coalesce(16)
will not have any impact at all as the one partition of your Dataframe is anyway located already on a single worker.
In order to increase parallelism you may want to use repartition(16)
instead.
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