The common query building pattern in HiveQL (and SQL in general) is to either select all columns (SELECT *) or an explicitly-specified set of columns (SELECT A, B, C). SQL has no built-in mechanism for selecting all but a specified set of columns.
There are various mechanisms for excluding some columns as outlined in this SO question but none apply naturally to HiveQL. (For example, the idea to create a temporary table with SELECT * then ALTER TABLE DROP some of its columns would wreak havoc in a big data environment.)
Ignoring the ideological discussion about whether it is a good idea to select all but some columns, this question is about the possible ways to extend Hive with this capability.
Prior to Hive 0.13.0 SELECT could take regular-expression-based columns, e.g., property_.* inside a backtick-quoted string. #invoketheshell's answer below refers to this capability but it comes at a cost, which is that, when this capability is on, Hive cannot accept columns with non-standard characters in them, e.g., $foo or x/y. That's why the Hive developers turned this behavior off by default in 0.13.0. I am looking for a generic solution that works for any column name.
A generic table-generating UDF (UDTF) could certainly do this because it can manipulate the schema. Since we are not going to generate new rows, is there a way to solve this problem using a simple row-based UDF?
This seems like a common problem with many posts around the Web showing how to solve it for various databases yet I haven't been able to find a solution for Hive. Is there code somewhere that does this?
You can choose every column except those listed in a regex based specification. This is query columns by exclusion. See below:
A SELECT statement can take regex-based column specification in Hive releases prior to 0.13.0, or in 0.13.0 and later releases if the configuration property hive.support.quoted.identifiers is set to none.
That being said you could create a new table or view using the following, and all the columns except the columns specified will be returned:
hive.support.quoted.identifiers=none;
drop table if exists database.table_name;
create table if not exists database.table_name as
select `(column_to_remove_1|...|column_to_remove_N)?+.+`
from database.some_table
where
--...
;
This will create a table that has all the columns from some_table except the columns named column_to_remove_1, ... , to column_to_remove_N. You can also choose to create a view instead.
Related
I have a Databricks delta table of financial transactions that is essentially a running log of all changes that ever took place on each record. Each record is uniquely identified by 3 keys. So given that uniqueness, each record can have multiple instances in this table. Each representing a historical entry of a change(across one or more columns of that record) Now if I wanted to find out cases where a specific column value changed I can easily achieve that by doing something like this -->
SELECT t1.Key1, t1.Key2, t1.Key3, t1.Col12 as "Before", t2.Col12 as "After"
from table1 t1 inner join table t2 on t1.Key1= t2.Key1 and t1.Key2 = t2.Key2
and t1.Key3 = t2.Key3 where t1.Col12 != t2.Col12
However, these tables have a large amount of columns. What I'm trying to achieve is a way to identify any columns that changed in a self-join like this. Essentially a list of all columns that changed. I don't care about the actual value that changed. Just a list of column names that changed across all records. Doesn't even have to be per row. But the 3 keys will always be excluded, since they uniquely define a record.
Essentially I'm trying to find any columns that are susceptible to change. So that I can focus on them dedicatedly for some other purpose.
Any suggestions would be really appreciated.
Databricks has change data feed (CDF / CDC) functionality that can simplify these type of use cases. https://docs.databricks.com/delta/delta-change-data-feed.html
I am new to Cassandra, and found below in the wikipedia.
A column family (called "table" since CQL 3) resembles a table in an RDBMS (Relational Database Management System). Column families contain rows and columns. Each row is uniquely identified by a row key. Each row has multiple columns, each of which has a name, value, and a timestamp. Unlike a table in an RDBMS, different rows in the same column family do not have to share the same set of columns, and a column may be added to one or multiple rows at any time.[29]
It said that 'different rows in the same column family do not have to share the same set of columns', but how to implement it? I have almost read all the documents in the offical site.
I can create table and insert data like below.
CREATE TABLE Emp_record(E_id int PRIMARY KEY,E_score int,E_name text,E_city text);
INSERT INTO Emp_record(E_id, E_score, E_name, E_city) values (101, 85, 'ashish', 'Noida');
INSERT INTO Emp_record(E_id, E_score, E_name, E_city) values (102, 90, 'ankur', 'meerut');
It's very like I did in the relational database. So how to create multiply rows with different columns?
I also found the offical document mentioned 'Flexible schema', how to understand it here?
Thanks very much in advance.
Column family is from the original design of Cassandra, when the data model looked like the Google BigTable or Apache HBase, and Thrift protocol was used for communication. But this required that schema was defined inside the application, and that makes access to data from many applications more problematic, as you need to update the schema inside all of them...
The CREATE TABLE and INSERT is a part of the Cassandra Query Language (CQL) that was introduced long time ago, and replaced Thrift-based implementation (Cassandra 4.0 completely removed the Thrift support). In CQL you need to have schema defined for a table, where you need to provide column name & type. If you really need to have dynamic columns, there are several approaches to that (I'll link answers that I already wrote over the time, so there won't duplicates):
If you have values of the same type, you can use one column as a name of the attribute/column, and another to store the value, like described here
if you have values of different types, you can also use one column as a name of attribute/column, and define multiple columns for values - one for each of the data types: int, text, ..., and you insert value into the corresponding columns only (described here)
you can use maps (described here) - it's similar to first or second, but mostly designed for very small number of "dynamic columns", plus have other limitations, like, you need to read the full map to fetch one value, etc.)
How does spark structured streaming let the sink know that a new row is an update of an existing row when run in an update mode? Does it look at all the values of all columns of the new row and an existing row for an equality match or does it compute some sort of hash?
Reading the documentation, we see some interesting information about update mode (bold formatting added by me):
Update Mode - Only the rows that were updated in the Result Table since the last trigger will be written to the external storage (available since Spark 2.1.1). Note that this is different from the Complete Mode in that this mode only outputs the rows that have changed since the last trigger. If the query doesn’t contain aggregations, it will be equivalent to Append mode.
So, to use update mode there needs to be some kind of aggregation otherwise all data will simply be added to the end of the result table. In turn, to use aggregation the data need to use one or more coulmns as a key. Since a key is needed it is easy to know if a row has been updated or not - simply compare the values with the previous iteration of the table (the key tells you which row to compare with). In aggregations that contains a groupby, the columns being grouped on are the keys.
Simple aggregations that return a single value will not require a key. However, since only a single value is returned it will update if that value is changed. An example here could be taking the sum of a column (without groupby).
The documentation contains a picture that gives a good understanding of this, see the "Model of the Quick Example" from the link above.
We have a use case to change cassandra table column (change the type from Int to Long), since it not supported changing from Int to varInt is supported and we are fine with that.
But in some of the tables this column is a cluster column and we have no way of changing this.
I am curious what is the best way to handle this case.
You can not alter a clustering column in Cassandra - you'll need to make a new table and load the data into that table using a third party application (cqlsh COPY being the simplest, or something like Spark). If you're unable to tolerate a change in the table's name, you'll need to backup your data, drop the old table, and recreate it with the proper types.
I have inserted string and integer values into dynamic columns in a Cassandra Column Family. When I query for the values in CQL they are displayed as hex encoded bits.
Can I somehow tell the query to decode the value into a string or integer?
I also would be happy to do this in the CLI if that's easier. There I see you can specify assume <column_family> validator as <type>;, but that applies to all columns and they have different types, so I have to run the assumption and query many times.
(Note that the columns are dynamic, so I haven't specified the validator when creating the column family).
You can use ASSUME in cqlsh like in cassandra-cli (although it only applies to printing values, not sending them, but that ought to be ok for you). You can also use it on a per-column basis, like:
ASSUME <column_family> ('anchor:cnnsi.com') VALUES ARE text;
..although (a), I just tested it, and this functionality is broken in cassandra-1.1.1 and later. I posted a fix at CASSANDRA-4352. And (b), this probably isn't a very versatile or helpful solution for more than a few one-off uses. I'd strongly recommend using CQL 3 here, as CQL direct support for wide storage engine rows like this is deprecated. Your table here is certainly adaptable to an (easier to use) CQL 3 model, but I couldn't say exactly what it would be without knowing more about how you're using it.