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
Related
I got a lot of data regarding stock prices and I want to try Apache Cassandra out for this purpose. But I'm not quite familiar with the primary/ partition/ clustering keys.
My database columns would be:
Stock_Symbol
Price
Timestamp
My users will always filter for the Stock_Symbol (where stock_symbol=XX) and then they might filter for a certain time range (Greater/ Less than (equals)). There will be around 30.000 stock symbols.
Also, what is the big difference when using another "filter", e.g. exchange_id (only two stock exchanges are available).
Exchange_ID
Stock_Symbol
Price
Timestamp
So my users would first filter for the stock exchange (which is more or less a foreign key), then for the stock symbol (which is also more or less a foreign key). The data would be inserted/ written in this order as well.
How do I have to choose the keys?
The Quick Answer
Based on your use-case and predicted query pattern, I would recommend one of the following for your table:
PRIMARY KEY (Stock_Symbol, Timestamp)
The partition key is made of Stock_Symbol, and Timestamp is the only clustering column. This will allow WHERE to be used with those two fields. If either are to be filtered on, filtering on Stock_Symbol will be required in the query and must come as the first condition to WHERE.
Or, for the second case you listed:
PRIMARY KEY ((Exchange_ID, Stock_Symbol), Timestamp)
The partition key is composed of Exchange_ID and Stock_Symbol, and Timestamp is the only clustering column. This will allow WHERE to be used with those three fields. If any of those three are to be filtered on, filtering on both Exchange_ID and Stock_Symbol will be required in the query and must come in that order as the first two conditions to WHERE.
See the last section of this answer for a few other variations that could also be applied based on your needs.
Long Answer & Explanation
Primary Keys, Partition Keys, and Clustering Columns
Primary keys in Cassandra, similar to their role in relational databases, serve to identify records and index them in order to access them quickly. However, due to the distributed nature of records in Cassandra, they serve a secondary purpose of also determining which node that a given record should be stored on.
The primary key in a Cassandra table is further broken down into two parts - the Partition Key, which is mandatory and by default is the first column in the primary key, and optional clustering column(s), which are all fields that are in the primary key that are not a part of the partition key.
Here are some examples:
PRIMARY KEY (Exchange_ID)
Exchange_ID is the sole field in the primary key and is also the partition key. There are no additional clustering columns.
PRIMARY KEY (Exchange_ID, Timestamp, Stock_Symbol)
Exchange_ID, Timestamp, and Stock_Symbol together form a composite primary key. The partition key is Exchange_ID and Timestamp and Stock_Symbol are both clustering columns.
PRIMARY KEY ((Exchange_ID, Timestamp), Stock_Symbol)
Exchange_ID, Timestamp, and Stock_Symbol together form a composite primary key. The partition key is composed of both Exchange_ID and Timestamp. The extra parenthesis grouping Exchange_ID and Timestamp group them into a single composite partition key, and Stock_Symbol is a clustering column.
PRIMARY KEY ((Exchange_ID, Timestamp))
Exchange_ID and Timestamp together form a composite primary key. The partition key is composed of both Exchange_ID and Timestamp. There are no clustering columns.
But What Do They Do?
Internally, the partitioning key is used to calculate a token, which determines on which node a record is stored. The clustering columns are not used in determining which node to store the record on, but they are used in determining order of how records are laid out within the node - this is important when querying a range of records. Records whose clustering columns are similar in value will be stored close to each other on the same node; they "cluster" together.
Filtering in Cassandra
Due to the distributed nature of Cassandra, fields can only be filtered on if they are indexed. This can be accomplished in a few ways, usually by being a part of the primary key or by having a secondary index on the field. Secondary indexes can cause performance issues according to DataStax Documentation, so it is typically recommended to capture your use-cases using the primary key if possible.
Any field in the primary key can have a WHERE clause applied to it (unlike unindexed fields which cannot be filtered on in the general case), but there are some stipulations:
Order Matters - The primary key fields in the WHERE clause must be in the order that they are defined; if you have a primary key of (field1, field2, field3), you cannot do WHERE field2 = 'value', but rather you must include the preceding fields as well: WHERE field1 = 'value' AND field2 = 'value'.
The Entire Partition Key Must Be Present - If applying a WHERE clause to the primary key, the entire partition key must be given so that the cluster can determine what node in the cluster the requested data is located in; if you have a primary key of ((field1, field2), field3), you cannot do WHERE field1 = 'value', but rather you must include the full partition key: WHERE field1 = 'value' AND field2 = 'value'.
Applied to Your Use-Case
With the above info in mind, you can take the analysis of how users will query the database, as you've done, and use that information to design your data model, or more specifically in this case, the primary key of your table.
You mentioned that you will have about 30k unique values for Stock_Symbol and further that it will always be included in WHERE cluases. That sounds initially like a resonable candidate for a partition key, as long as queries will include only a single value that they are searching for in Stock_Symbol (e.g. WHERE Stock_Symbol = 'value' as opposed to WHERE Stock_Symbol < 'value'). If a query is intended to return multiple records with multiple values in Stock_Symbol, there is a danger that the cluster will need to retrieve data from multiple nodes, which may result in performance penalties.
Further, if your users wish to filter on Timestamp, it should also be a part of the primary key, though wanting to filter on a range indicates to me that it probably shouldn't be a part of the partition key, so it would be a good candidate for a clustering column.
This brings me to my recommendation:
PRIMARY KEY (Stock_Symbol, Timestamp)
If it were important to distribute data based on both the Stock_Symbol and the Timestamp, you could introduce a pre-calculated time-bucketed field that is based on the time but with less cardinality, such as Day_Of_Week or Month or something like that:
PRIMARY KEY ((Stock_Symbol, Day_Of_Week), Timestamp)
If you wanted to introduce another field to filtering, such as Exchange_ID, it could be a part of the partition key, which would mandate it being included in filters, or it could be a part of the clustering column, which would mean that it wouldn't be required unless subsequent fields in the primary key needed to be filtered on. As you mentioned that users will always filter by Exchange_ID and then by Stock_Symbol, it might make sense to do:
PRIMARY KEY ((Exchange_ID, Stock_Symbol), Timestamp)
Or to make it non-mandatory:
PRIMARY KEY (Stock_Symbol, Exchange_ID, Timestamp)
I am new to Cassandra and have a few novice level questions in the primary key.
Is the Primary key supposed to be unique per record? (My guess would be not.)
To elaborate. Suppose my table looks like this
CREATE TABLE user_action (
user_id int,
action text,
date_of_action date,
PRIMARY KEY (user_id)
)
I am guessing I can have multiple rows with the same user_id
If primary key is not one per record, can a primary key be split across many partitions?
Can a partition have multiple primary keys?
Is the primary key itself decided to pick the partition or is the hashCode of the primary key used to pick a partition?
Is it fair to think of a partition as a file?
Primary key and Partition key in some case would be the same but not always, it depends upon the number of primary keys. Data is distributing based on partition key which is unique across the Cassandra cluster. I am not explaining all the scenario and concept here but yes, you should go through the documentation and I am sure you can understand the things very quick after reading the below link.
https://www.datastax.com/blog/2016/02/most-important-thing-know-cassandra-data-modeling-primary-key
https://docs.datastax.com/en/dse/5.1/cql/cql/cql_using/useCompoundPrimaryKeyConcept.html
1>Is the Primary key supposed to be unique per record? (My guess would
be not.) To elaborate. Suppose my table looks like this
CREATE TABLE user_action ( user_id int, action text, date_of_action
date, PRIMARY KEY (user_id) )
Primary key is supposed to be unique per record /row. In the example you mentioned, you can have only one record for user_id. For allowing multiple rows with same user_id, you have to introduce a differentiating key. This key is called clustering key in Cassandra and it forms a part of primary key.
Primary key is a combination of (partition key and clustering key(s)). Partition key is used by Cassandra to find a partition/record. If clustering key is defined in data model then it will be used to differentiate different rows. If no clustering key is defined as in your case then only one record will be kept in database.
In example below you can have same user_id records who live different states. Here Primary key is combination of (user_id, state). user_id is the partition key and state is clustering key.
CREATE TABLE user_action (
user_id int,
state text,
action text,
date_of_action date,
PRIMARY KEY (user_id,state)
)
I am guessing I can have multiple rows with the same user_id
As explained above you can have multiple rows with the same user_id if you define a clustering key otherwise with the example you quoted, it is not possible.
2>If primary key is not one per record, can a primary key be split
across many partitions?
Primary key cannot be split across many partitions. As explained above partition key part of primary key will always point to unique partition.
3>Can a partition have multiple primary keys?
In the example I have quoted, (1,RJ), (1,GJ) can be possible primary keys pointing towards single partition pointed by parition key value 1. So you can have multiple primary keys for a partitions in that sense.
4>Is the primary key itself decided to pick the partition or is the
hashCode of the primary key used to pick a partition?
Hashcode of partition key (part of primary key) is used to get the partition
5>Is it fair to think of a partition as a file?
It will depend on your data model.
I am trying to create a Cassandra table where i store the logs for a shop as per the timestamp. I also want to create a query which returns the data in a descending order with respect to the timestamp. If I make my timestamp as the primary key it will be automatically be the partition key as i don't have any other columns as composite primary key.
And in Cassandra we can't do ORDER BY on partition keys. Is there any way that I make my timestamp as primary key and not as partition key (A Cassandra DB without a partition key).
Thanks in advance.
table creation if required :
CREATE TABLE myCass.logs(timestamp timestamp, logs text, PRIMARY KEY (timestamp));
Since you have the timestamp you know the year, month, day. You could use those as your partition key and have the timestamp as a clustering column. In this way you would satisfy also the need for a partition key, you will have a primary key for the data, you could order by on timestamps and you would evenly spread your data across the cluster.
This way of splitting data is called bucketing. Here is some good reading on this subject - Cassandra Time Series Data Modeling For Massive Scale
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 am new to Cassandra, I am confused between rowkey and partition key in Cassandra.
I am creating a table like:
Create table events( day text, hour text, dip text, sip text, count counter,
primary key((day,hour), dip, sip));
As per my understanding, in the above table day and hour columns form a partition key and dip,sip columns form a clustering key.
My understanding is that row key is nothing but partition key i.e. day, hour columns form a row key.
Is my understanding correct? Can any one clarify this?
Is my understanding correct, Can any one clarify this?
Yes, your understanding is correct. The row key is the "old school" way of referring to a partition key. The partition key (as you probably understand) is the part of the CQL PRIMARY KEY which determines where the data is stored in the cluster. In your case, data within your partition keys will be sorted by dip and sip (your clustering keys).
You should give John Berryman's article Understanding How CQL3 Maps To Cassandra’s Internal Data Structure a read. It does a great job of explaining how your table structures map "under the hood."