We want to create table in cassandra keyspace and decided to make only one column as primary key (As a result, that column is the partition key with no clustering key).
For example in 'sample' table, only 'id' column is primary key and so every partition has only one row.
create table sample(id int primary key, name text);
What are the Advantages and Disadvantages when take every row as one partition?
Related
Good Evening,
my problem is, that my recent understanding for partition and primary key is, that the partition key is to distribute the data between the nodes, and the primary ALWAYS contains the partition key. I want to create a partition key to cluster the data with duplicate partition keys and in these clusters I want to have a primary key for unique rows. In my first understanding of Cassandra, it could be possible if can take apart the partition and primary key. Is this possible?
An example to ease my idea:
country
state
unique_id
USA
TEXAS
123
USA
TEXAS
114
country and state as the partition key and the unique id as the primary key.
If I create the primary key like this: PRIMARY KEY ((country, state,unique_id)) I can't filter without using the unique_id but I want e.g. a query like SELECT unique_id FROM table WHERE state = 'Texas' and country = 'USA'.
If I create the primary key in this way: PRIMARY KEY ((country, state)), it obviously overwrites the data every time one entry gets inserted with the same country and state that's why I need the unique primary key.
Primary key always includes the partition key, that's always a first item in the primary key. Partition key could consist out of multiple columns, that's why you have brackets around first item in your example. I believe that in your case, primary key should be as following:
PRIMARY KEY ((country, state),unique_id)
In this case, partition key is a combination of country + state, and then inside that partition you will have unique IDs that will be used to select specific items. General syntax for primary key is:
partition key, clustering column1, clustering column2, ...
where partition key could be either:
column - single column
(column1, column2, ...) - multiple columns
I have the following scenario;
My data has the id field, and this field constantly increasing.
When event created, id is assigned = 1 automatically.
Then 2, 3, 4 and so on.
When data that has the id = 1 is generated, then it will never be generated again.
I want to store this dat ain Cassandra. I can set primary key as the id field, but i dont know how cassandra will create partitions for each record?
Will it create one partition for each record?
Or will it create range partition by primary key. For example; id from 1 to 100 is the first partition, 100-200 is the second partition etc.
In Cassandra, the partition key uniquely identifies a single partition (record) in the table. For clarification, the primary key:
must have 1 partition key
zero or more clustering columns
So the primary key doesn't equate to a range of partitions.
Compared to traditional RDBMS which have two-dimensional tables, Cassandra tables have the traditional 2D tables but can also be 3D or more. The power of Cassandra is that tables can be multi-dimensional meaning each partition can have one or more rows (it can have thousands).
If you're interested, I've explained this in a bit more detail with examples in this post -- https://community.datastax.com/questions/6171/. Cheers!
I'm trying to understand the scenario when no clustering key is specified in a table definition.
If a table has only a partition key and no clustering key, what order the rows under the same partition are stored in? Is it even allowed to have multiple rows under the same partition when no clustering key exists? I tried searching for it online but couldn't get a clear explanation.
I got the below explanation from Cassandra user group so posting it here in case someone else is looking for the same info:
"Note that a table always has a partition key, and that if the table has
no clustering columns, then every partition of that table is only
comprised of a single row (since the primary key uniquely identifies
rows and the primary key is equal to the partition key if there is no
clustering columns)."
http://cassandra.apache.org/doc/latest/cql/ddl.html#the-partition-key
Before I`ve found good explanation of keys in Cassandra:
Difference between partition key, composite key and clustering key in Cassandra?.
Now I am reading about partitioner and there I can see term "row key". What is the row key? How can I list it with CQL?
The row key is just another name for the PRIMARY KEY. It is the combination of all the partition and clustering fields, and it will map to just one row of data in a table. So when you do a read or write to a particular row key, it will access just one row.
In terms of the partitioner, that only uses the partition key fields, and it generates a token hash value that determines which node in a cluster the partition will be stored on. Individual rows are stored within partitions, so if there are no clustering columns, then the partition will hold a single row and the row key would be the same as the partition key.
If you have clustering columns, then you can store multiple rows within a partition and the row key will be the partition key plus the clustering key.
I've read in some posts that having duplicate partitioning key can have a performance impact. I've two tables like:
CREATE TABLE "Test1" ( CREATE TABLE "Test2" (
key text, key text,
column1 text, name text,
value text, age text,
PRIMARY KEY (key, column1) ...
) PRIMARY KEY (key, name,age)
)
In Test1 column1 will contain column name and value will contain its corresponding value.The main advantage of Test1 is that I can add any number of column/value pairs to it without altering the table by just providing same partitioning key each time.
Now my question is how will each of these table schema's impact the read/write performance if I've millions of rows and number of columns can be upto 50 in each row. How will it impact the compaction/repair time if I'm writing duplicate entries frequently?
For efficient queries, you want to hit a parition (i.e. have the first key of your primary key in your query). Inside of your partition, each column is stored in sorted form by the respective clustering keys. Cassandra stores data as "map of sorted maps".
Your Test1 schema will allow you to fetch all columns for a key, or a specific column for a key. Each "entry" will be on a separate parition.
For Test2, you can query by key, (key and name), or (key, name and age). But you won't be able to get to the age for a key without also specifying the name (w/o adding a secondary index). For this schema too, each "entry" will be in its own partition.
Cross partition queries are more expensive than those that hit a single partition. If you're looking for simply key-value lookups, then either schema will suffice. I wouldn't be worried using either for 50 columns. The first will give you direct access to a particular column. The latter will give you access to the whole data for an entry.
What you should focus more on is which structure allows you to do the queries you want. The first won't be very useful for secondary indexes, but the second will, for example.