I'm currently trying to create txt files from all tables in the dbo schema
I have like 200s-300s tables there, so it would takes up too much times to create it manually..
I was thinking for creating a loop.
so as example (using AdventureWorks2019) :
select t.name as table_name
from sys.tables t
where schema_name(t.schema_id) = 'Person'
order by table_name;
This would get all the table name within the Person schema.
So I would loop :
Table input : select * from ${table_name}
But then i realized that for txt files, i need to declare all the field and their data types in pentaho, so it would become a problems.
Any ideas how to do this "backup" txt files?
Using Metadata Injection and more queries to the schema catalog tables in SQL Server. You not only need to retrieve the table name, you would need to afterwards retrieve the columns in that table and the data types, and inject that information (metadata) to the text output step.
You have in the samples directory of your spoon installation an example on how to use Metadata Injection, use it, along with the documentation, to build a simple example (the check to generate a transformation with the metadata you have injected is of great use to debug)
I have something similar to copy data from one database to another, both in Oracle, but with SQL Server you have similar catalog tables as in Oracle to retrieve the information you need. I created a simple, almost empty transformation to read one table and write to another. This transformation has almost no information, only the database origin in the Table Input step and the target database in the Table Output step:
And then I have a second transformation where I fill up all the information (metadata) to inject: The query to perform in the Table Input step, and all the data I need in the Table Output: Target table, if I need to truncate before inserting, the columns from (stream field) and to (Table field):
I have a directory in HDFS, everyday one processed file is placed in that directory with DateTimeStamp in file name, if I create external table on top of that Directory location, does external table refreshes itself when every day file comes and resides in that directory ??
If you add files into table directory or partition directory, does not matter, external or managed table in Hive, the data will be accessible for queries, you do not need to do any additional steps to make data available, no refresh is necessary.
Hive table/partition is a metadata (DDL, location, statistics, access permissions, etc) plus data files in the location. So, data is stored in the table/partition location in HDFS.
Only if you create new directory for new partition which is not created yet, then you will need to execute ALTER TABLE ADD PARTITION LOCATION=<new location> or MSCK REPAIR TABLE command. The equivalent command on Amazon Elastic MapReduce (EMR)'s version of Hive is: ALTER TABLE table_name RECOVER PARTITIONS.
If you add files into already created table/partition locations, no refresh is necessary.
CBO can use statistics for query calculation without reading data files, for example count(*). It works for simple queries only, like count(*), max().
If you are using CBO with statistics for query calculation, you may need to refresh it using ANALYZE TABLE hive_table PARTITION(partitioned_col) COMPUTE STATISTICS. See this answer for more details: https://stackoverflow.com/a/39914232/2700344
If you do not need statistics and want your table location to be scanned every time you query it, switch it off: set hive.compute.query.using.stats=false;
If I build a Hive table on top of some S3 (or HDFS) directory like so:
create external table newtable (name string)
row format delimited
fields terminated by ','
stored as textfile location 's3a://location/subdir/';
When I add files to that S3 location, the Hive table doesn't automatically update. The new data is only included if I create a new Hive table on that location. Is there a way to build a Hive table (maybe using partitions) so that whenever new files are added to the underlying directory, the Hive table automatically shows that data (without having to recreate the Hive table)?
On HDFS each file scanned each time table being queried as #Dudu Markovitz pointed. And files in HDFS are immediately consistent.
Update: S3 is also strongly consistent now, so removed part about eventual consistency.
Also there may be a problem with using statistics when querying table after adding files, see here: https://stackoverflow.com/a/39914232/2700344
Everything #leftjoin says is correct, with one extra detail: s3 doesn't offer immediate consistency on listings. A new blob can be uploaded, HEAD/GET will return it but a list operation on the parent path may not see it. This means that Hive code which lists the directory may not see the data. Using unique names doesn't fix this, only using a consistent DB like Dynamo which is updated as files are added/removed. Even there, you have added a new thing to keep in sync...
I Want to know the sourse of this table. How it is calculated from the table. I am using sql server r2 2008 and I searched for that table, but it is not there. It is formed by manipulating some rows of different tables. Is there any way to find it. I searched the corresponding table in VB 6 also. but it is not there. Is there Any way to find the source table?
Source in local variables is :
"Select * From #70554TempShiz52"
Tables with name starting with # or ## are temporary tables (Quick Overview: Temporary Tables in SQL Server 2005).
The table exists only as long as the connection in which table was created exists. It is accessible only from connection which has created it.
To find the table you should look for an appropriate statement CREATE TABLE #70554TempShiz52 in the code.
The table is exists in tempdb database. An admin can see it there using ssms (only when the connection is still open and table was not dropped). I usually put a breakpoint to achieve desired state. The name of the table looks like #70554TempShiz52__________...some number (to distinguish tables from other users).
I can be useful to use a name starting with ## for debugging because such a table is visible from other connections.
Can anyone tell me the difference between Hive's external table and internal tables.
I know the difference comes when dropping the table. I don't understand what you mean by the data and metadata is deleted in internal and only metadata is deleted in external tables.
Can anyone explain me in terms of nodes please.
Hive has a relational database on the master node it uses to keep track of state.
For instance, when you CREATE TABLE FOO(foo string) LOCATION 'hdfs://tmp/';, this table schema is stored in the database.
If you have a partitioned table, the partitions are stored in the database(this allows hive to use lists of partitions without going to the file-system and finding them, etc). These sorts of things are the 'metadata'.
When you drop an internal table, it drops the data, and it also drops the metadata.
When you drop an external table, it only drops the meta data. That means hive is ignorant of that data now. It does not touch the data itself.
Hive tables can be created as EXTERNAL or INTERNAL. This is a choice that affects how data is loaded, controlled, and managed.
Use EXTERNAL tables when:
The data is also used outside of Hive. For example, the data files are read and processed by an existing program that doesn't lock the files.
Data needs to remain in the underlying location even after a DROP TABLE. This can apply if you are pointing multiple schemas (tables or views) at a single data set or if you are iterating through various possible schemas.
You want to use a custom location such as ASV.
Hive should not own data and control settings, dirs, etc., you have another program or process that will do those things.
You are not creating table based on existing table (AS SELECT).
Use INTERNAL tables when:
The data is temporary.
You want Hive to completely manage the lifecycle of the table and data.
To answer you Question :
For External Tables, Hive stores the data in the LOCATION specified during creation of the table(generally not in warehouse directory). If the external table is dropped, then the table metadata is deleted but not the data.
For Internal tables, Hive stores data into its warehouse directory. If the table is dropped then both the table metadata and the data will be deleted.
For your reference,
Difference between Internal & External tables :
For External Tables -
External table stores files on the HDFS server but tables are not linked to the source file completely.
If you delete an external table the file still remains on the HDFS server.
As an example if you create an external table called “table_test” in HIVE using HIVE-QL and link the table to file “file”, then deleting “table_test” from HIVE will not delete “file” from HDFS.
External table files are accessible to anyone who has access to HDFS file structure and therefore security needs to be managed at the HDFS
file/folder level.
Meta data is maintained on master node, and deleting an external table from HIVE only deletes the metadata not the data/file.
For Internal Tables-
Stored in a directory based on settings in hive.metastore.warehouse.dir,
by default internal tables are stored in the following directory “/user/hive/warehouse” you can change it by updating the location in the config file .
Deleting the table deletes the metadata and data from master-node and HDFS respectively.
Internal table file security is controlled solely via HIVE. Security needs to be managed within HIVE, probably at the schema level (depends
on organization).
Hive may have internal or external tables, this is a choice that affects how data is loaded, controlled, and managed.
Use EXTERNAL tables when:
The data is also used outside of Hive. For example, the data files are read and processed by an existing program that doesn’t lock the files.
Data needs to remain in the underlying location even after a DROP TABLE. This can apply if you are pointing multiple schema (tables or views) at a single data set or if you are iterating through various possible schema.
Hive should not own data and control settings, directories, etc., you may have another program or process that will do those things.
You are not creating table based on existing table (AS SELECT).
Use INTERNAL tables when:
The data is temporary.
You want Hive to completely manage the life-cycle of the table and data.
Source :
HDInsight: Hive Internal and External Tables Intro
Internal & external tables in Hadoop- HIVE
An internal table data is stored in the warehouse folder, whereas an external table data is stored at the location you mentioned in table creation.
So when you delete an internal table, it deletes the schema as well as the data under the warehouse folder, but for an external table it's only the schema that you will loose.
So when you want an external table back you again after deleting it, can create a table with the same schema again and point it to the original data location. Hope it is clear now.
The only difference in behaviour (not the intended usage) based on my limited research and testing so far (using Hive 1.1.0 -cdh5.12.0) seems to be that when a table is dropped
the data of the Internal (Managed) tables gets deleted from the HDFS file system
while the data of the External tables does NOT get deleted from the HDFS file system.
(NOTE: See Section 'Managed and External Tables' in https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL which list some other difference which I did not completely understand)
I believe Hive chooses the location where it needs to create the table based on the following precedence from top to bottom
Location defined during the Table Creation
Location defined in the Database/Schema Creation in which the table is created.
Default Hive Warehouse Directory (Property hive.metastore.warehouse.dir in hive.site.xml)
When the "Location" option is not used during the "creation of a hive table", the above precedence rule is used. This is applicable for both Internal and External tables. This means an Internal table does not necessarily have to reside in the Warehouse directory and can reside anywhere else.
Note: I might have missed some scenarios, but based on my limited exploration, the behaviour of both Internal and Extenal table seems to be the same except for the one difference (data deletion) described above. I tried the following scenarios for both Internal and External tables.
Creating table with and without Location option
Creating table with and without Partition Option
Adding new data using the Hive Load and Insert Statements
Adding data files to the Table location outside of Hive (using HDFS commands) and refreshing the table using the "MSCK REPAIR TABLE command
Dropping the tables
In external tables, if you drop it, it deletes only schema of the table, table data exists in physical location. So to deleted the data use hadoop fs - rmr tablename .
Managed table hive will have full control on tables. In external tables users will have control on it.
INTERNAL : Table is created First and Data is loaded later
EXTERNAL : Data is present and Table is created on top of it.
Internal tables are useful if you want Hive to manage the complete lifecycle of your data including the deletion, whereas external tables are useful when the files are being used outside of Hive.
External hive table has advantages that it does not remove files when we drop tables,we can set row formats with different settings , like serde....delimited
Also Keep in mind that Hive is a big data warehouse. When you want to drop a table you dont want to lose Gigabytes or Terabytes of data. Generating, moving and copying data at that scale can be time consuming.
When you drop a 'Managed' table hive will also trash its data.
When you drop a 'External' table only the schema definition from hive meta-store is removed. The data on the hdfs still remains.
Consider this scenario which best suits for External Table:
A MapReduce (MR) job filters a huge log file to spit out n sub log files (e.g. each sub log file contains a specific message type log) and the output i.e n sub log files are stored in hdfs.
These log files are to be loaded into Hive tables for performing further analytic, in this scenario I would recommend an External Table(s), because the actual log files are generated and owned by an external process i.e. a MR job besides you can avoid an additional step of loading each generated log file into respective Hive table as well.
The best use case for an external table in the hive is when you want to create the table from a file either CSV or text
Both Internal and External tables are owned by HIVE. The only difference being the ownership of data. The commands for creating both tables are shown below. Only an additional EXTERNAL keyword comes in case of external table creation. Both tables can be created/deleted/modified using SQL Statements.
In case of Internal Tables, both the table and the data contained in the tables are managed by HIVE. That is, we can add/delete/modify any data using HIVE. When we DROP the table, along with the table, the data will also get deleted.
Eg: CREATE TABLE tweets (text STRING, words INT, length INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE;
In case of External Tables, only the table is managed by HIVE. The data present in these tables can be from any storage locations like HDFS. We cant add/delete/modify the data in these tables. We can only use the data in these tables using SELECT statements. When we DROP the table, only the table gets deleted and not the data contained in it. This is why its said that only meta-data gets deleted. When we create EXTERNAL tables, we need to mention the location of the data.
Eg: CREATE EXTERNAL TABLE tweets (text STRING, words INT, length INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE LOCATION '/user/hive/warehouse/tweets';
hive stores only the meta data in metastore and original data in out side of hive when we use external table we can give location' ' by these our original data wont effect when we drop the table
When there is data already in HDFS, an external Hive table can be created to describe the data. It is called EXTERNAL because the data in the external table is specified in the LOCATION properties instead of the default warehouse directory.
When keeping data in the internal tables, Hive fully manages the life cycle of the table and data. This means the data is removed once the internal table is dropped. If the external table is dropped, the table metadata is deleted but the data is kept. Most of the time, an external table is preferred to avoid deleting data along with tables by mistake.
For managed tables, Hive controls the lifecycle of their data. Hive stores the data for managed tables in a sub-directory under the directory defined by hive.metastore.warehouse.dir by default.
When we drop a managed table, Hive deletes the data in the table.But managed tables are less convenient for sharing with other tools. For example, lets say we have data that is created and used primarily by Pig , but we want to run some queries against it, but not give Hive ownership of the data.
At that time, external table is defined that points to that data, but doesn’t take ownership of it.
In Hive We can also create an external table. It tells Hive to refer to the data that is at an existing location outside the warehouse directory.
Dropping External tables will delete metadata but not the data.
I would like to add that
Internal tables are used when the data needs to be updated or some rows need to be deleted because ACID properties can be supported on the Internal tables but ACID properties cannot be supported on the external tables.
Please ensure that there is a backup of the data in the Internal table because if a internal table is dropped then the data will also be lost.
In simple words, there are two things:
Hive can manage things in warehouse i.e. it will not delete data out of warehouse.
When we delete table:
1) For internal tables the data is managed internally in warehouse. So will be deleted.
2) For external tables the data is managed eternal from warehouse. So can't be deleted and clients other then hive can also use it.