I have the following table value type in SQL which is used in Azure Data Factory to import data from a flat file in a bulk copy activity via a stored procedure. File 1 has all three columns in it so this works fine. File 2 only has Column1 and Column2, but NOT Column3. I figured since the column was defined as NULL it would be ok but ADF complains that its attempting to pass in 2 columns when the table type expects 3. Is there a way to reuse this type for both files and make Column3 optional?
CREATE TYPE [dbo].[TestType] AS TABLE(
Column1 varchar(50) NULL,
Column2 varchar(50) NULL,
Column3 varchar(50) NULL
)
Operation on target LandSource failed:
ErrorCode=SqlOperationFailed,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=A
database operation failed with the following error: 'Trying to pass a
table-valued parameter with 2 column(s) where the corresponding
user-defined table type requires 3 column(s)
Would be nice if the copy activity behavior was consistent regardless of whether or not a stored procedure with table type is used or native BCP in the activity. When not using the table type and using the default bulk insert, missing columns in the source file end up being NULL in the target table without error (assumming the column is NULLABLE).
It will cause the mapping error in ADF.
In the Copy Activity, every column needs to be mapped.
If the source file only has two columns, it will cause mapping error.
So, I suggest you to create two different Copy activities and create a two columns table type.
You can pass optional column, I've made a test successfully, but the steps will be a bit complex. In my case, File 1 has all three columns, File 2 only has Column1 and Column2, but NOT Column3. It will use Get Metadata activity, Set Variable activity, ForEach activity, IfCondition activity.
Please follow my steps:
You need to define a variable FileName to foreach.
In the Get Metadata1 activity, I specified the file path.
In the ForEach1 activity, use #activity('Get Metadata1').output.childItems to foreach the filelist. It need to be Sequential.
Inside the ForEach1 activity, use Set Variable1 to set the FileName variable.
In the Get Metadata2, use item().name to specify the file.
In the Get Metadata2, use Column count to get the column count from the file.
In the If Contdition1, use #greater(activity('Get Metadata2').output.columnCount,2) to determine whether the file is larger than two columns.
In the True activity, use variable FileName to specify the file.
In the False activity, use Additional columns to add a Column.
When I run debug, the result shows:
I have a data set and want to import it into my database with the condition:
In case there is a record that cannot be imported, it can be extracted into a log
Although existing records can not be imported but still allow import of records that can be imported (other records) and continue to process
Currently I use the BCP utility to import data into the table from the csv file with:
bcp table_name IN C:\Users\09204086121\Desktop\data.csv -T -c -o C:\Users\09204086121\Desktop\logOut.log -e C:\Users\09204086121\Desktop\errOut.log
It just satisfies my condition 1 above.
I need that when the record has error (duplicate primary key,...), write to log (1) and continue to insert into the table the other normal records (2).
I came up with the idea that combining trigger with bcp, after creating a trigger and adding the parameter -h "FIRE_TRIGGERS" to the bcp statement, the insert will ignore records that have the same key but it won't write to the log.
This is my trigger.
ALTER TRIGGER [PKGORDERCOMMON].[T_ImportData] ON [PKGORDERCOMMON].[IF_R_BUNRUI1]
INSTEAD OF INSERT
AS
BEGIN
--Insert non duplicate records
INSERT INTO [IF_R_BUNRUI1]
(
SYSTEM_KB,
BUNRUI1_CD,
BUNRUI1_KANJI_NA,
BUNRUI1_KANA_NA,
CREATE_TS
)
SELECT SYSTEM_KB,
BUNRUI1_CD,
BUNRUI1_KANJI_NA,
BUNRUI1_KANA_NA,
CREATE_TS
FROM inserted i
WHERE NOT EXISTS
(
SELECT *
FROM [IF_R_BUNRUI1] c
WHERE c.BUNRUI1_CD = i.BUNRUI1_CD
AND c.SYSTEM_KB = i.SYSTEM_KB
);
END;
Is there anyone who can help me.
BCP is not meant for what you are asking it to do (separate good and bad records). For instance, bcp -e option has a limit to how many records it will show. Im not sure if this limit is tied to the "max errors" option, but regardless there is a limit.
Your best option is to load all the records and address bad data in t-sql.
Load all records in such a way to ignore conversion errors. Either:
load entire line from file into a single, large varchar column. Parse out columns and qc data as needed.
or
load all columns from source file into generic varchar columns with large enough size to accomodate your source data.
Either way, when done, use t-sql to inspect your data and split among good/bad records.
I created database using SQL in hive.
And I looked for the database using HDFS.
But I couldn't find database in HDFS.
In hive:
CREATE DATABASE practice
LOCATION '/user/hive/warehouse'/
Checking:
hdfs dfs -ls /user/hive/warehouse
There is nothing in warehouse.
In addition, I created a table in a specific database in hive.
But, using Hue, I could see the table in the default location.
I wanna insert the table into a specific database location.
CREATE TABLE prac (
id INT,
title STRING,
salary INT,
posted TIMESTAMP
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION '/user/hive/warehouse/practice.db/prac';
I couldn't find the table prac in the database practice in Hue and HDFS.
How can I see the database in HDFS?
And I also wanna know how to see the table in the specific database location.
Try by specifying db name while creating hive table prac by default hive creates tables in default database.
Example:
hive> CREATE DATABASE practice LOCATION '/user/hive/warehouse/practice.db';
hive> CREATE TABLE `practice.prac` ( id INT, title STRING, salary INT, posted TIMESTAMP ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION '/user/hive/warehouse/practice.db/prac';
Try using below command:
CREATE DATABASE practice
LOCATION '/user/hive/warehouse/practice.db';
hive by default uses '/user/hive/warehouse/' directory to create databases under this location. So while creating database, If you don't provide the location it will pick the database location like this '/user/hive/warehouse/practice.db'.
You can choose any location over hdfs until you have read and write permission over that location.
I have a possibly peculiar data migration problem:
I have an existing and populated SQLite3 database.
I do receive a new schema for a (hopefully compatible) database.
Result should be a new database, built according to new schema, containing as much as possible of the old database content.
Given limitations in both SQLite3 ALTER statement and our workflow it is safe to assume:
normal case will be new columns are added to end of table.
added columns (fields) will either have a default or can be left NULL.
rarely some table will be added.
very rarely some table or column may be dropped.
no table/column renaming will happen.
no column reshuffling will happen.
NOTE: if the new schema is not compatible with the old one (i.e.: any of the above assumptions does not hold true) it's accepted to fail badly.
I tried this script (old database is data.sql3 and new schema is data.schema):
mkdir tmp
cd tmp
#compute old DB schema
sqlite3 ../data.sql3 .schema >old_s
#purge new schema for any initialization...
grep -v ^INSERT ../data.schema >data.schema
#... create a dew, empty DB...
sqlite3 new.sql3 <data.schema
#... and compute a standard schema
#(this is done to avoid typing differences)
sqlite3 new.sql3 .schema >new_s
#iff the schemas are different
if ! diff -q old_s new_s
then
#save old DB
mv ../data.sql3 .
#dump contents
sqlite3 data.sql3 .dump >old_d
#expunge all statements needed to recreate DB/Tables
#new_d contains only INSERT statements
grep -v -f old_s old_d >new_d
#add old DB content to new DB
sqlite3 new.sql3 <new_d
#move new DB in place
mv new.sql3 ../data.sql3
fi
cd ..
This works to detect changes, but fails to repopulate the new database because .dump does not contain column names and thus insertion fails (missing value).
What I'm looking for is some way to force sqlite3 DB .dump to output INSERT statements containing all field names (normally it relies on position) or, it that's not possible, some way to tell sqlite3 DB <new_d to consider any undefined field as null or default (without failing).
Any other way to achieve the same result (without requiring knowledge of what, exactly, has been modified) would be equally welcome.
To be able to insert/import dumps with less columns into a table you can provide default values for the new, appended columns, or simply enable them to be set to NULL. The constraint clause is the same for CREATE TABLE and ALTER TABLE:
http://www.sqlite.org/syntax/column-constraint.html
-- newColumn is set to a default value if not provided with INSERT
alter table myTable
add column newColumn INTEGER NOT NULL default 0;
-- newColumn may be NULL, which is the default if not provided with INSERT
alter table myTable
add column newColumn INTEGER;
-- It is also valid to combine NULL and DEFAULT constraints
alter table myTable
add column newColumn INTEGER default 0;
Note that in order for the INSERT statement to work with the new columns it must provide the column names.
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I'm surprised this hasn't been posted yet. Any interesting tricks that you know about in Postgres? Obscure config options and scaling/perf tricks are particularly welcome.
I'm sure we can beat the 9 comments on the corresponding MySQL thread :)
Since postgres is a lot more sane than MySQL, there are not that many "tricks" to report on ;-)
The manual has some nice performance tips.
A few other performance related things to keep in mind:
Make sure autovacuum is turned on
Make sure you've gone through your postgres.conf (effective cache size, shared buffers, work mem ... lots of options there to tune).
Use pgpool or pgbouncer to keep your "real" database connections to a minimum
Learn how EXPLAIN and EXPLAIN ANALYZE works. Learn to read the output.
CLUSTER sorts data on disk according to an index. Can dramatically improve performance of large (mostly) read-only tables. Clustering is a one-time operation: when the table is subsequently updated, the changes are not clustered.
Here's a few things I've found useful that aren't config or performance related per se.
To see what's currently happening:
select * from pg_stat_activity;
Search misc functions:
select * from pg_proc WHERE proname ~* '^pg_.*'
Find size of database:
select pg_database_size('postgres');
select pg_size_pretty(pg_database_size('postgres'));
Find size of all databases:
select datname, pg_size_pretty(pg_database_size(datname)) as size
from pg_database;
Find size of tables and indexes:
select pg_size_pretty(pg_relation_size('public.customer'));
Or, to list all tables and indexes (probably easier to make a view of this):
select schemaname, relname,
pg_size_pretty(pg_relation_size(schemaname || '.' || relname)) as size
from (select schemaname, relname, 'table' as type
from pg_stat_user_tables
union all
select schemaname, relname, 'index' as type
from pg_stat_user_indexes) x;
Oh, and you can nest transactions, rollback partial transactions++
test=# begin;
BEGIN
test=# select count(*) from customer where name='test';
count
-------
0
(1 row)
test=# insert into customer (name) values ('test');
INSERT 0 1
test=# savepoint foo;
SAVEPOINT
test=# update customer set name='john';
UPDATE 3
test=# rollback to savepoint foo;
ROLLBACK
test=# commit;
COMMIT
test=# select count(*) from customer where name='test';
count
-------
1
(1 row)
The easiest trick to let postgresql perform a lot better (apart from setting and using proper indexes of course) is just to give it more RAM to work with (if you have not done so already). On most default installations the value for shared_buffers is way too low (in my opinion). You can set
shared_buffers
in postgresql.conf. Divide this number by 128 to get an approximation of the amount of memory (in MB) postgres can claim. If you up it enough this will make postgresql fly. Don't forget to restart postgresql.
On Linux systems, when postgresql won't start again you will probably have hit the kernel.shmmax limit. Set it higher with
sysctl -w kernel.shmmax=xxxx
To make this persist between boots, add a kernel.shmmax entry to /etc/sysctl.conf.
A whole bunch of Postgresql tricks can be found here:
http://www.postgres.cz/index.php/PostgreSQL_SQL_Tricks
Postgres has a very powerful datetime handling facility thanks to its INTERVAL support.
For example:
select NOW(), NOW() + '1 hour';
now | ?column?
-------------------------------+-------------------------------
2009-04-18 01:37:49.116614+00 | 2009-04-18 02:37:49.116614+00
(1 row)
select current_date ,(current_date + interval '1 year')::date;
date | date
---------------------+----------------
2014-10-17 | 2015-10-17
(1 row)
You can cast many strings to an INTERVAL type.
COPY
I'll start. Whenever I switch to Postgres from SQLite, I usually have some really big datasets. The key is to load your tables with COPY FROM rather than doing INSERTS. See documentation:
http://www.postgresql.org/docs/8.1/static/sql-copy.html
The following example copies a table to the client using the vertical bar (|) as the field delimiter:
COPY country TO STDOUT WITH DELIMITER '|';
To copy data from a file into the country table:
COPY country FROM '/usr1/proj/bray/sql/country_data';
See also here:
Faster bulk inserts in sqlite3?
My by far favorite is generate_series: at last a clean way to generate dummy rowsets.
Ability to use a correlated value in a LIMIT clause of a subquery:
SELECT (
SELECT exp_word
FROM mytable
OFFSET id
LIMIT 1
)
FROM othertable
Abitlity to use multiple parameters in custom aggregates (not covered by the documentation): see the article in my blog for an example.
One of the things I really like about Postgres is some of the data types supported in columns. For example, there are column types made for storing Network Addresses and Arrays. The corresponding functions (Network Addresses / Arrays) for these column types let you do a lot of complex operations inside queries that you'd have to do by processing results through code in MySQL or other database engines.
Arrays are really cool once you get to know 'em.
Lets say you would like to store some hyper links between pages. You might start by thinking about creating a Table kinda like this:
CREATE TABLE hyper.links (
tail INT4,
head INT4
);
If you needed to index the tail column, and you had, say 200,000,000 links-rows (like wikipedia would give you), you would find yourself with a huge Table and a huge Index.
However, with PostgreSQL, you could use this Table format instead:
CREATE TABLE hyper.links (
tail INT4,
head INT4[],
PRIMARY KEY(tail)
);
To get all heads for a link you could send a command like this (unnest() is standard since 8.4):
SELECT unnest(head) FROM hyper.links WHERE tail = $1;
This query is surprisingly fast when it is compared with the first option (unnest() is fast and the Index is way way smaller). Furthermore, your Table and Index will take up much less RAM-memory and HD-space, especially when your Arrays are so long that they are compressed to a Toast Table. Arrays are really powerful.
Note: while unnest() will generate rows out of an Array, array_agg() will aggregate rows into an Array.
Materialized Views are pretty easy to setup:
CREATE VIEW my_view AS SELECT id, AVG(my_col) FROM my_table GROUP BY id;
CREATE TABLE my_matview AS SELECT * FROM my_view;
That creates a new table, my_matview, with the columns and values of my_view. Triggers or a cron script can then be setup to keep the data up to date, or if you're lazy:
TRUNCATE my_matview;
INSERT INTO my_matview SELECT * FROM my_view;
Inheritance..infact Multiple Inheritance (as in parent-child "inheritance" not 1-to-1 relation inheritance which many web frameworks implement when working with postgres).
PostGIS (spatial extension), a wonderful add-on that offers comprehensive set of geometry functions and coordinates storage out of the box. Widely used in many open-source geo libs (e.g. OpenLayers,MapServer,Mapnik etc) and definitely way better than MySQL's spatial extensions.
Writing procedures in different languages e.g. C, Python,Perl etc (makes your life easir to code if you're a developer and not a db-admin).
Also all procedures can be stored externally (as modules) and can be called or imported at runtime by specified arguments. That way you can source control the code and debug the code easily.
A huge and comprehensive catalogue on all objects implemented in your database (i.e. tables,constraints,indexes,etc).
I always find it immensely helpful to run few queries and get all meta info e.g. ,constraint names and fields on which they have been implemented on, index names etc.
For me it all becomes extremely handy when I have to load new data or do massive updates in big tables (I would automatically disable triggers and drop indexes) and then recreate them again easily after processing has finished. Someone did an excellent job of writing handful of these queries.
http://www.alberton.info/postgresql_meta_info.html
Multiple schemas under one database, you can use it if your database has large number of tables, you can think of schemas as categories. All tables (regardless of it's schema) have access to all other tables and functions present in parent db.
You don't need to learn how to decipher "explain analyze" output, there is a tool: http://explain.depesz.com
select pg_size_pretty(200 * 1024)
pgcrypto: more cryptographic functions than many programming languages' crypto modules provide, all accessible direct from the database. It makes cryptographic stuff incredibly easy to Just Get Right.
A database can be copied with:
createdb -T old_db new_db
The documentation says:
this is not (yet) intended as a general-purpose "COPY DATABASE" facility
but it works well for me and is much faster than
createdb new_db
pg_dump old_db | psql new_db
Memory storage for throw-away data/global variables
You can create a tablespace that lives in the RAM, and tables (possibly unlogged, in 9.1) in that tablespace to store throw-away data/global variables that you'd like to share across sessions.
http://magazine.redhat.com/2007/12/12/tip-from-an-rhce-memory-storage-on-postgresql/
Advisory locks
These are documented in an obscure area of the manual:
http://www.postgresql.org/docs/9.0/interactive/functions-admin.html
It's occasionally faster than acquiring multitudes of row-level locks, and they can be used to work around cases where FOR UPDATE isn't implemented (such as recursive CTE queries).
This is my favorites list of lesser know features.
Transactional DDL
Nearly every SQL statement is transactional in Postgres. If you turn off autocommit the following is possible:
drop table customer_orders;
rollback;
select *
from customer_orders;
Range types and exclusion constraint
To my knowledge Postgres is the only RDBMS that lets you create a constraint that checks if two ranges overlap. An example is a table that contains product prices with a "valid from" and "valid until" date:
create table product_price
(
price_id serial not null primary key,
product_id integer not null references products,
price numeric(16,4) not null,
valid_during daterange not null
);
NoSQL features
The hstore extension offers a flexible and very fast key/value store that can be used when parts of the database need to be "schema-less". JSON is another option to store data in a schema-less fashion and
insert into product_price
(product_id, price, valid_during)
values
(1, 100.0, '[2013-01-01,2014-01-01)'),
(1, 90.0, '[2014-01-01,)');
-- querying is simply and can use an index on the valid_during column
select price
from product_price
where product_id = 42
and valid_during #> date '2014-10-17';
The execution plan for the above on a table with 700.000 rows:
Index Scan using check_price_range on public.product_price (cost=0.29..3.29 rows=1 width=6) (actual time=0.605..0.728 rows=1 loops=1)
Output: price
Index Cond: ((product_price.valid_during #> '2014-10-17'::date) AND (product_price.product_id = 42))
Buffers: shared hit=17
Total runtime: 0.772 ms
To avoid inserting rows with overlapping validity ranges a simple (and efficient) unique constraint can be defined:
alter table product_price
add constraint check_price_range
exclude using gist (product_id with =, valid_during with &&)
Infinity
Instead of requiring a "real" date far in the future Postgres can compare dates to infinity. E.g. when not using a date range you can do the following
insert into product_price
(product_id, price, valid_from, valid_until)
values
(1, 90.0, date '2014-01-01', date 'infinity');
Writeable common table expressions
You can delete, insert and select in a single statement:
with old_orders as (
delete from orders
where order_date < current_date - interval '10' year
returning *
), archived_rows as (
insert into archived_orders
select *
from old_orders
returning *
)
select *
from archived_rows;
The above will delete all orders older than 10 years, move them to the archived_orders table and then display the rows that were moved.
1.) When you need append notice to query, you can use nested comment
SELECT /* my comments, that I would to see in PostgreSQL log */
a, b, c
FROM mytab;
2.) Remove Trailing spaces from all the text and varchar field in a database.
do $$
declare
selectrow record;
begin
for selectrow in
select
'UPDATE '||c.table_name||' SET '||c.COLUMN_NAME||'=TRIM('||c.COLUMN_NAME||') WHERE '||c.COLUMN_NAME||' ILIKE ''% '' ' as script
from (
select
table_name,COLUMN_NAME
from
INFORMATION_SCHEMA.COLUMNS
where
table_name LIKE 'tbl%' and (data_type='text' or data_type='character varying' )
) c
loop
execute selectrow.script;
end loop;
end;
$$;
3.) We can use a window function for very effective removing of duplicate rows:
DELETE FROM tab
WHERE id IN (SELECT id
FROM (SELECT row_number() OVER (PARTITION BY column_with_duplicate_values), id
FROM tab) x
WHERE x.row_number > 1);
Some PostgreSQL's optimized version (with ctid):
DELETE FROM tab
WHERE ctid = ANY(ARRAY(SELECT ctid
FROM (SELECT row_number() OVER (PARTITION BY column_with_duplicate_values), ctid
FROM tab) x
WHERE x.row_number > 1));
4.) When we need to identify server's state, then we can use a function:
SELECT pg_is_in_recovery();
5.) Get functions's DDL command.
select pg_get_functiondef((select oid from pg_proc where proname = 'f1'));
6.) Safely changing column data type in PostgreSQL
create table test(id varchar );
insert into test values('1');
insert into test values('11');
insert into test values('12');
select * from test
--Result--
id
character varying
--------------------------
1
11
12
You can see from the above table that I have used the data type – ‘character varying’ for ‘id’
column. But it was a mistake, because I am always giving integers as id. So using varchar here is a
bad practice. So let’s try to change the column type to integer.
ALTER TABLE test ALTER COLUMN id TYPE integer;
But it returns:
ERROR: column “id” cannot be cast automatically to type integer SQL
state: 42804 Hint: Specify a USING expression to perform the
conversion
That means we can’t simply change the data type because data is already there in the column. Since the data is of type ‘character varying’ postgres cant expect it as integer though we entered integers only. So now, as postgres suggested we can use the ‘USING’ expression to cast our data into integers.
ALTER TABLE test ALTER COLUMN id TYPE integer USING (id ::integer);
It Works.
7.) Know who is connected to the Database
This is more or less a monitoring command. To know which user connected to which database
including their IP and Port use the following SQL:
SELECT datname,usename,client_addr,client_port FROM pg_stat_activity ;
8.) Reloading PostgreSQL Configuration files without Restarting Server
PostgreSQL configuration parameters are located in special files like postgresql.conf and pg_hba.conf. Often, you may need to change these parameters. But for some parameters to take effect we often need to reload the configuration file. Of course, restarting server will do it. But in a production environment it is not preferred to restarting the database, which is being used by thousands, just for setting some parameters. In such situations, we can reload the configuration files without restarting the server by using the following function:
select pg_reload_conf();
Remember, this wont work for all the parameters, some parameter
changes need a full restart of the server to be take in effect.
9.) Getting the data directory path of the current Database cluster
It is possible that in a system, multiple instances(cluster) of PostgreSQL is set up, generally, in different ports or so. In such cases, finding which directory(physical storage directory) is used by which instance is a hectic task. In such cases, we can use the following command in any database in the cluster of our interest to get the directory path:
SHOW data_directory;
The same function can be used to change the data directory of the cluster, but it requires a server restarts:
SET data_directory to new_directory_path;
10.) Find a CHAR is DATE or not
create or replace function is_date(s varchar) returns boolean as $$
begin
perform s::date;
return true;
exception when others then
return false;
end;
$$ language plpgsql;
Usage: the following will return True
select is_date('12-12-2014')
select is_date('12/12/2014')
select is_date('20141212')
select is_date('2014.12.12')
select is_date('2014,12,12')
11.) Change the owner in PostgreSQL
REASSIGN OWNED BY sa TO postgres;
12.) PGADMIN PLPGSQL DEBUGGER
Well explained here
It's convenient to rename an old database rather than mysql can do. Just using the following command:
ALTER DATABASE name RENAME TO new_name