I'm trying to migrate data to QuestDB and inserting historical records, I create table as
create table records(
type INT,
interval INT,
timestamp TIMESTAMP,
name STRING) timestamp(timestamp)
and insert data from CSV by curl uploading it.
I get back error "Cannot insert rows out of order". I read that out of order was supported in QuestDB but somehow I cannot make it work.
You can insert rows out of order on partitioned tables only, create new partitioned table and copy data into it
create table records2(
type INT,
interval INT,
timestamp TIMESTAMP,
name STRING
)
timestamp(timestamp) partition by DAY
insert into records2
select * from records
drop table records
rename table records2 to records
After this you'll be able to insert out of order into table records
Related
I want to limit the number of records in my SQLite table to for example 100 records, and then when I INSERT the 101th record, the first record (the oldest) be removed from the table. In other word, I want to prevent the table from growing more than 100 records and always have the last 100 records. Is there any setting or query with SQLite or should I handle it manually?
thanks in advance
You can do it with a trigger.
Say that your table is this:
CREATE TABLE tablename (
id INTEGER PRIMARY KEY,
name TEXT,
inserted_at TEXT DEFAULT (strftime('%Y-%m-%d %H:%M:%f', 'now'))
);
In the column inserted_at you will have the timestamp of the insertion of each row.
This is not necessary if you declared the column id as:
id INTEGER PRIMARY KEY AUTOINCREMENT
because in this case you could identify the 1st inserted row by the minimum value of the id.
Now create this trigger:
CREATE TRIGGER keep_100_rows AFTER INSERT ON tablename
WHEN (SELECT COUNT(*) FROM tablename) > 100
BEGIN
DELETE FROM tablename
WHERE id = (SELECT id FROM tablename ORDER BY inserted_at, id LIMIT 1);
-- or if you define id as AUTOINCREMENT
-- WHERE id = (SELECT MIN(id) FROM tablename);
END;
END;
Every time that you insert a new row, the trigger will check if the table has more than 100 rows and if it does it will delete the 1st inserted row.
See the demo (for max 3 rows).
I have query pertaining to the google big query tables. We are currently looking to query the big query table based on the file uploaded on the day into the cloud storage.
Meaning:
I have to load the data into big query table based on every day's data into cloud storage.
When i query:
select * from BQT where load_date =<TODAY's DATE>
Can we achieve this without adding the date field into the file?
If you just don't want to add a date column, Append current date suffix to your table name like BQT_20200112 when the GCS file is uploaded.
Then you can query specific datetime table by _TABLE_SUFFIX syntax.
Below is example query using _TABLE_SUFFIX
SELECT
field1,
field2,
field3
FROM
`your_dataset.BQT_*`
WHERE
_TABLE_SUFFIX = '20200112'
As you see, You don't need to add additional field like load_date when you query the tables using date suffix and wildcard symbol.
Athena Tables are partitioned like and same as s3 folder path
parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=17
parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=9
parent=0fc966a0-bba7-4c0b-a648-cff7f0332059/year=2020/month=4/date=16
parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=14
PARTITIONED BY (
`parent` string,
`year` int,
`month` tinyint,
`date` tinyint)
Now how should I form the where condition for a select query to get data for parent = "9ab4fcca-65d8-11ea-bc55-0242ac130003" from 2019-06-01 to 2020-04-31 ?
SELECT *
FROM table
WHERE parent = '9ab4fcca-65d8-11ea-bc55-0242ac130003' AND year >= 2019 AND year <= 2020 AND month >= 04 AND month <= 06 AND date >= 01 AND date <= 31 ;
But this isn't correct. Please help
Partitioning on year, month, and day separately makes it unnecessarily difficult to query tables. If you're starting out I really suggest to avoid this kind of partitioning scheme. If you can't avoid it you can still make things easier by creating the table partitions differently.
Most guides will tell you to create directory structures like year=2020/month=4/date=1/file1, create a table with three corresponding partition columns, and then run MSCK REPAIR TABLE to load partitions. This works, but it's far from the best way to use Athena. MSCK REPAIR TABLE has atrocious performance, and partitioning like that is far from ideal.
I suggest creating directory structures that are just 2020-03-01/file1, but if you can't, you can actually have any structure you want, 2020/03/01/file1, year=2020/month=4/date=1/file1, or any other structure where there is one distinct prefix per date will work more or less equally well.
I also suggest you create tables with only one partition column: date (or dt or day if you want avoid quoting), typed as DATE, not string.
What you do then, instead of running MSCK REPAIR TABLE is that you use ALTER TABLE … ADD PARTITION or the Glue APIs directly, to add partitions. This command lets you specify the location separately from the partition column value:
ALTER TABLE my_table ADD
PARTITION (day = '2020-04-01') LOCATION 's3://some-bucket/path/to/2020-04-01/'
The important thing here is that the partition column value doesn't have to have any relationship at all with the location, this would work equally well:
ALTER TABLE my_table ADD
PARTITION (day = '2020-04-01') LOCATION 's3://some-bucket/path/to/data-for-first-of-april/'
For your specific case you could have:
PARTITIONED BY (`parent` string, `day` date)
and then do:
ALTER TABLE your_table ADD
PARTITION (parent = '9ab4fcca-65d8-11ea-bc55-0242ac130003', day = '2020-04-17') LOCATION 's3://your-bucket/parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=17'
PARTITION (parent = '9ab4fcca-65d8-11ea-bc55-0242ac130003', day = '2020-04-09') LOCATION 's3://your-bucket/parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=9'
PARTITION (parent = '0fc966a0-bba7-4c0b-a648-cff7f0332059', day = '2020-04-16') LOCATION 's3://your-bucket/parent=0fc966a0-bba7-4c0b-a648-cff7f0332059/year=2020/month=4/date=16'
PARTITION (parent = '9ab4fcca-65d8-11ea-bc55-0242ac130003', day = '2020-04-14') LOCATION 's3://your-bucket/parent=9ab4fcca-65d8-11ea-bc55-0242ac130003/year=2020/month=4/date=14'
Here is how you can use year, month and day values the come from partitions in order to select date range
SELECT col1, col2
FROM my_table
WHERE CAST(date_parse(concat(CAST(year AS VARCHAR(4)),'-',
CAST(month AS VARCHAR(2)),'-',
CAST(day AS VARCHAR(2))
), '%Y-%m-%d') as DATE)
BETWEEN DATE '2019-06-01' AND DATE '2020-04-31'
You can add additional filter statements as needed)
I'm trying to remove a column from a partitioned table in BigQuery using this command
bq query --destination_table [DATASET].[TABLE_NAME] --replace --use_legacy_sql=false 'SELECT * EXCEPT(column) FROM [DATASET].[TABLE_NAME]'
As a result the unwanted column is removed, the schema is changed but the data is no more partitioned.
Any suggestion on how to keep the data partitioned after the column is removed? Docs are clear only for non partitioned tables.
There are two workarounds you can use:
Use a column-partitioning table, which means it's partitioned on a value of a regular column in a table. You can create a new column-partitioned table and copy the data deleting the column:
bq mk --time_partitioning_field=pt --schema=... [DATASET].[TABLE_NAME2]
bq query --destination_table=[DATASET].[TABLE_NAME2] "SELECT _PARTITIONTIME as pt, * EXCEPT(column) from [DATASET].[TABLE_NAME]"
You can also still use day-partitioned tables, but copy the data using DML. You can set or copy _PARTITIONTIME column inside the DML INSERT statement, which is not possible with regular SELECT. Here is an example:
INSERT INTO
dataset1.table1 (_partitiontime,
a,
b)
SELECT
TIMESTAMP(DATE "2008-12-25") AS _partitiontime,
"a" AS a,
"b" AS b
This requires DML over partitioned tables, which is currently in alpha: https://issuetracker.google.com/issues/36383555
BigQuery now supports DROP COLUMN in partitioned tables:
ALTER TABLE mydataset.mytable
DROP COLUMN column
It's in beta at the time of writing, but it worked for me.
How to alter column data type in Amazon Redshift database?
I am not able to alter the column data type in Redshift; is there any way to modify the data type in Amazon Redshift?
As noted in the ALTER TABLE documentation, you can change length of VARCHAR columns using
ALTER TABLE table_name
{
ALTER COLUMN column_name TYPE new_data_type
}
For other column types all I can think of is to add a new column with a correct datatype, then insert all data from old column to a new one, and finally drop the old column.
Use code similar to that:
ALTER TABLE t1 ADD COLUMN new_column ___correct_column_type___;
UPDATE t1 SET new_column = column;
ALTER TABLE t1 DROP COLUMN column;
ALTER TABLE t1 RENAME COLUMN new_column TO column;
There will be a schema change - the newly added column will be last in a table (that may be a problem with COPY statement, keep that in mind - you can define a column order with COPY)
to avoid the schema change mentioned by Tomasz:
BEGIN TRANSACTION;
ALTER TABLE <TABLE_NAME> RENAME TO <TABLE_NAME>_OLD;
CREATE TABLE <TABLE_NAME> ( <NEW_COLUMN_DEFINITION> );
INSERT INTO <TABLE_NAME> (<NEW_COLUMN_DEFINITION>)
SELECT <COLUMNS>
FROM <TABLE_NAME>_OLD;
DROP TABLE <TABLE_NAME>_OLD;
END TRANSACTION;
(Recent update) It's possible to alter the type for varchar columns in Redshift.
ALTER COLUMN column_name TYPE new_data_type
Example:
CREATE TABLE t1 (c1 varchar(100))
ALTER TABLE t1 ALTER COLUMN c1 TYPE varchar(200)
Here is the documentation link
If you don't want to change the column order, an option will be creating a temp table, drop & create the new one with desired size and then bulk again the data.
CREATE TEMP TABLE temp_table AS SELECT * FROM original_table;
DROP TABLE original_table;
CREATE TABLE original_table ...
INSERT INTO original_table SELECT * FROM temp_table;
The only problem recreating the table is that you will need to grant again permissions and if the table is too bigger it will take a piece of time.
ALTER TABLE publisher_catalogs ADD COLUMN new_version integer;
update publisher_catalogs set new_version = CAST(version AS integer);
ALTER TABLE publisher_catalogs DROP COLUMN version RESTRICT;
ALTER TABLE publisher_catalogs RENAME new_version to version;
Redshift being columnar database doesn't allow you to modify the datatype directly,
however below is one approach this will change the column order.
Steps -
1.Alter table add newcolumn to the table
2.Update the newcolumn value with oldcolumn value
3.Alter table to drop the oldcolumn
4.alter table to rename the columnn to oldcolumn
If you don't want to alter the order of the columns then solution would be to
1.create temp table with new column name
copy data from old table to new table.
drop old table
rename the newtable to oldtable
One important thing create a new table using like command instead simple create.
This method works for converting an (big) int column into a varchar
-- Create a backup of the original table
create table original_table_backup as select * from original_table;
-- Drop the original table, and then recreate with new desired data types
drop table original_table;
create table original_table (
col1 bigint,
col2 varchar(20) -- changed from bigint
);
-- insert original entries back into the new table
insert into original_table select * from original_table_backup;
-- cleanup
drop original_table_backup;
You can use the statements below:
ALTER TABLE <table name --etl_proj_atm.dim_card_type >
ALTER COLUMN <col name --card_type> type varchar(30)
UNLOAD and COPY with table rename strategy should be the most efficient way to do this operation if retaining the table structure(row order) is important.
Here is an example adding to this answer.
BEGIN TRANSACTION;
ALTER TABLE <TABLE_NAME> RENAME TO <TABLE_NAME>_OLD;
CREATE TABLE <TABLE_NAME> ( <NEW_COLUMN_DEFINITION> );
UNLOAD ('select * from <TABLE_NAME>_OLD') TO 's3://bucket/key/unload_' manifest;
COPY <TABLE_NAME> FROM 's3://bucket/key/unload_manifest'manifest;
END TRANSACTION;
for updating the same column in redshift this would work fine
UPDATE table_name
SET column_name = 'new_value' WHERE column_name = 'old_value'
you can have multiple clause in where by using and, so as to remove any confusion for sql
cheers!!