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 want to use the IN clause for the non-primary key column in Cassandra. Is it possible? if it is not is there any alternate or suggestion?
Three possible solutions
Create a secondary index. This is not recommended due to performance problems.
See if you can designate that column in the existing table as part of the primary key
Create another denormalised table that table is optimised for your query. i.e data model by query pattern
Update:
And also even after you move that to primary key, operations with IN clause can be further optimised. I found this cassandra lookup by list of primary keys in java very useful
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
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."
I'm trying to understand the difference between these two and the scenarios in which you would prefer to use one over the other.
My specific use case is using cassandra as an event ingestion system backed by an analytics engine that interprets the event.
My model includes
event id (the partition key)
event time (a clustering column)
event type (i'm not sure whether to use clustering column or secondary index)
I figure the most common read scenario will be to get the events over a time range hence event time is the clustering column. A less frequent read scenario might involve further filtering the event query by event type.
A secondary index is pretty similar to what we know from regular relational databases. If you have a query with a where clause that uses column values that are not part of the primary key, lookup would be slow because a full row search has to be performed. Secondary indexes make it possible to service such queries efficiently. Secondary indexes are stored as extra tables, and just store extra data to make it easy to find your way in the main table.
So that's a good ol' index, which we already know about. So far, there's nothing new to cassandra and its distributed nature.
Partitioning and clustering is all about deciding how rows from the main table are spread among the nodes. This is unique to cassandara since it determines the distribution of data. So, the primary key consists of at least one column. The first column in the primary key is used as the partition key. The partition key is used to decide which node to store a row. If the primary key has additional columns, the columns are used to cluster the data on a given node - the data is stored in lexicographic order on a node by clustering columns.
This question has more specifics on clustering columns: Clustering Keys in Cassandra
So an index on a given column X makes the lookup X --> primary key efficient. The partition key (first column in the primary key) determines which node a row is stored on. Clustering columns (additional columns in the primary key) determine which order rows are stored in on their assigned node.
So your intuition sounds about right - the event ID is presumably guaranteed unique, so is great for building a primary key. Event time is a great way to order rows on disk on a given node.
If you never needed to lookup data by event type, eg, never had a query like SELECT * FROM Events WHERE Type = Warning, then you have no need for your additional indexes, but your demands for partitioning don't change. Indexes make it easy to serve queries with different predicates. Since you mentioned that you indeed were planning on performing queries like that, you do in fact likely want an index on your EventType column.
Check out the cassandra documentation: http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_compound_keys_c.html
Cassandra uses the first column name in the primary key definition as the partition key.
...
In the case of the playlists table, the song_order is the clustering column. The data for each partition is clustered by the remaining column or columns of the primary key definition. On a physical node, when rows for a partition key are stored in order based on the clustering columns