I have two tables one is users and other is expired_users.
users columns-> id, name, age
expired_users columns -> id, name
I want to execute the following query.
delete from users where id in (select id from expired_users);
This query works fine with SQL related databases. I want find a solution to solve this in cassandra.
PS: I don't want to add any extra columns in the tables.
While designing cassandra data model, we cannot think exactly like RDBMS .
Design like this --
create table users (
id int,
name text,
age int,
expired boolean static,
primary key (id,name)
);
To mark a user as expired -- Just insert the same row again
insert into users (id,name,age,expired) values (100,'xyz',80,true);
you don't have to update or delete the row, just insert it again, previous column values will get overridden.
What you want to is to use join as a filter for your delete statement, and this is not what the Cassandra model is built for.
AFAIK there is no way to perform this using cql. If you want to perform this action without changing the schema - run external script in any language that has drivers for Cassandra.
Related
I have a very simple data table. But after reading a lot of examples in the internet, I am still more and more confused how to solve the following scenario:
1) The Table
My data table looks like this (without defining the primayr key, as this is my understanding problem):
CREATE TABLE documents (
uid text,
created text,
data text
}
Now my goal is to have to different ways to select data.
2) Select by the UID:
SELECT * FROM documents
WHERE uid = ‘xxxx-yyyyy-zzzz’
3) Select by a date limit
SELECT * FROM documents
WHERE created >= ‘2015-06-05’
So my question is:
What should my table definition in Cassandra look like, so that I can perform these selections?
To achieve both queries, you would need two tables.
First one would look like:
CREATE TABLE documents (
uid text,
created text,
data text,
PRIMARY KEY (uid));
and you retrieve your data with: SELECT * FROM documents WHERE uid='xxxx-yyyy-zzzzz' Of course, uid must be unique. You might want to consider the uuid data type (instead of text)
Second one is more delicate. If you set your partition to the full date, you won't be able to do a range query, as range query is only available on the clustering column. So you need to find the sweet spot for your partition key in order to:
make sure a single partition won't be too large (max 100MB,
otherwise you will run into trouble)
satisfy your query requirements.
As an example:
CREATE TABLE documents_by_date (
year int,
month int,
day int,
uid text,
data text,
PRIMARY KEY ((year, month), day, uid);
This works fine if within a day, you don't have too many documents (so your partition don't grow too much). And this allows you to create queries such as: SELECT * FROM documents_by_date WHERE year=2018 and month=12 and day>=6 and day<=24; If you need to issue a range query across multiple months, you will need to issue multiple queries.
If your partition is too large due to the data field, you will need to remove it from documents_by_date. And use documents table to retrieve the data, given the uid you retreived from documents_by_date.
If your partition is still too large, you will need to add hour in the partition key of documents_by_date.
So overall, it's not a straightforward request, and you will need to find the right balance for yourself when defining your partition key.
If latency is not a huge concern, an alternative would be to use the stratio lucene cassandra plugin, and index your date.
Question does not specify how your data is going to be with respect user and create time. But since its a document, I am assuming that one user will be creating one document at one "created" time.
Below is the table definition you can use.
CREATE TABLE documents (
uid text,
created text,
data text
PRIMARY KEY (uid, created)
) WITH CLUSTERING ORDER BY (created DESC);
WITH CLUSTERING ORDER BY (created DESC) can help you get the data order by created for a given user.
For your first requirement you can query like given below.
SELECT * FROM documents WHERE uid = 'SEARCH_UID';
For your second requirement you can query like given below
SELECT * FROM documents WHERE created > '2018-04-10 11:32:00' ALLOW FILTERING;
Use of Allow Filtering should be used diligently as it scans all partitions. If we have to create a separate table with date as primary key, it becomes tricky if there are many documents being inserted at very same second. Clustering order works best for the requirements where documents for a given user need to be sorted by time.
Does anyone know of an efficient way to fetch counter data stored in a separate table for each row gotten in a query?
The tables are defined as follows
TABLE person (
id timeuuid,
name text,
many other attributes... );
TABLE person_counts(
id timeuuid, //same id as person
count1 counter,
PRIMARY KEY (id));
The goal is that when persons/single person are fetched, before returning to add the count then return. Is iterating over each person and querying person_counts the only way to achieve this? It needs to be a counter however since I need a certain Primary Key for the person table I cannot have a counter directly there it seems.
I am using datastax cassandra if it makes a difference.
For this insertion, you can use the batch operation, which is an atomic operation in cassandra datastax driver. Whenever you try to enter a record in persons table, you have to create a prepared query for persons table and persons_count table, and add the two prepared queries to a single batch and carry on the insertion. The advantage with batches is, it is an atomic operation i.e., either it inserts both records or none at all.
In the same way whenever you want to delete from persons table, create a batch and delete from both the persons and persons_count table. You can read more about them here:https://datastax.github.io/cpp-driver/topics/basics/batches/
Note: The two tables are independent and you can read entries of the two tables separately.Inserting via batch operation does not make them interlinked.
Now, for the requirement of fetching, you have to first query the count from the table and then go to the persons table. Probably, there is no other way as cassandra doesn't support any joins. Moreover, you have to specify the primary key for the persons table and other attributes, which help in finalising whether count should be in other table or it can be use in the same table. If you are fine with this implementation, you can use this:
create table persons(id uuid,
name text, count counter, primary key(id, name));
and update statement for the counter column. Then, there is no need of other table too.
I am new to cassandra and am coming from Postgres. I was wondering if there is a way that I can get data from 2 different tables or column family and then return the results. I have this query
select p.fullname,p.picture s.post, s.id, s.comments, s.state, s.city FROM profiles as p INNER JOIN Chats as s ON(p.id==s.profile_id) WHERE s.latitudes>=28 AND 29>= s.latitudes AND s.longitudes
">=-21 AND -23>= s.longitudes
The query has 2 tables: Profiles and Chat and they both share a common field Chats.id==Proifles.profile_id it boils down to this basically return all rows where Chat ID is equal to Profiles id. I would like to keep it that way because now updating profiles are simple and would only need to update 1 row per profile update instead of de-normalizing everything and updating thousands of records. Any help or suggestions would be great
You have to design tables in way you won't need joins. Best practice is if your table matches exactly the use case it is used for.
Cassadra has a feature called shared static columns; this allows you to bind values with partition part of primary key. Thus, you can create "joined" version of table without duplicates.
CREATE TABLE t (
p_id uuid,
p_fullname text STATIC,
p_picture text STATIC,
s_id uuid,
s_post text,
s_comments text,
s_state text,
s_city text,
PRIMARY KEY (p_id, s_id)
);
I have requirement where I need to find top ranked pictures in chronological order from certain city. I came up with below schema
create table top_picture(
picture_id uuid,
city text,
rank int,
date timestamp,
primary key (city,date,rank)
) with CLUSTERING ORDER BY (date desc,rank desc);
It does solve problem to some extent (apart from duplicates) by executing following query
select * from top_picture where city='san diego';
. But if same picture_id is inserted in same day then I get duplicate entries as picture_id is not part of partition key. However I can not add it to partitioning key because then I won't be able make simple selection query like above as I would need to provide picture_id with selection query and it won't give top pics for city.
Did anyone came accross this type of schema before or any other recommended ways to do it.
It sounds like you want two views of the data. In one view you want to get the top ranked pictures and in the other view you want the picture_id to be unique.
So you could have two tables, with one that has picture_id as the primary key and the other as you have shown.
When you have a picture to insert, you would first try to insert it into the picture_id table using the IF NOT EXISTS clause on the insert statement. If that insert fails, then it is a duplicate and you would not insert it into the top_picture table.
In Cassandra 3.0 there is going to be support for materialized views like this, but for now you would have to manage both tables in your application code.
I'm having trouble designing a column family that suits the following requirement:
I would like to update X rows that match some condition for a field that is not the primary key and is not unique.
For example if a User column family has ID, name and birthday columns, I would like to update all the users that were born after some specific day.
Even if I add the 'birthday' to the primary key (lets say 'ID', 'birthday') I cannot perform this query because part of the primary key is missing.
How can i approach this by designing my column family differently ?
Thanks.
According to cassandra docs, there is no way to update rows without explicitly defining their partition key. This was done not by an accident, but because this feature (e.g. update users set status=1 where id>10) can allow user to update all data in table at once, which can be very-very-very expensive on large databases. Cassandra explicitly forbids all operations requiring data scans within multiple partitions.
To update multiple users all at once, you have to know their IDs. Having a table defined as:
CREATE TABLE stackoverflow.users (
id timeuuid PRIMARY KEY,
dob timestamp,
status text
)
and knowing user's primary key, you can run queries like update users set status='foo' where id in (1,2,3,4). But queries with really large sets of keys inside IN statement may cause performance issues on C*.
But how can you have an efficient range query like select id from some_table where dob>'2000-01-01 00:00:01'? There are two options available, and both of them are not really acceptable:
Create an index table like
CREATE TABLE stackoverflow.dob_index (
year int,
dob timestamp,
ids list<timeuuid>,
PRIMARY KEY (year, dob)
)
with compound partition+clustering primary key and use multiple queries like select * from dob_index where year=2014 and dob<'2014-05-01 00:00:01'; to fetch ids for different years. Notice that I've defined multiple partitions for the table to have some kind of even partition distribution in cluster. But the general idea is that you really shouldn't have a small amount of very large partitions. Prefer a large amount of small ones, if there's a choice.
Have a separate stand-alone index available for complex queries (like ElasticSearch/Solr/Sphinx).
But I suggest you to revisit your application logic in a way to avoid updating/deleting data at all:
instead of updating users table directly, you can have a separate table user_status you insert new statuses:
CREATE TABLE user_statuses (
id timeuuid,
updated_at timestamp,
status text,
PRIMARY KEY (id, updated_at)
)
When you need to scan/update a lot of rows at once, prefer using tools like Spark to efficiently distribute your workload among your cluster nodes.