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| Logical Data Modeling |
| ===================== |
| |
| Now that you have defined your queries, you’re ready to begin designing |
| Cassandra tables. First, create a logical model containing a table |
| for each query, capturing entities and relationships from the conceptual |
| model. |
| |
| To name each table, you’ll identify the primary entity type for which you |
| are querying and use that to start the entity name. If you are querying |
| by attributes of other related entities, append those to the table |
| name, separated with ``_by_``. For example, ``hotels_by_poi``. |
| |
| Next, you identify the primary key for the table, adding partition key |
| columns based on the required query attributes, and clustering columns |
| in order to guarantee uniqueness and support desired sort ordering. |
| |
| The design of the primary key is extremely important, as it will |
| determine how much data will be stored in each partition and how that |
| data is organized on disk, which in turn will affect how quickly |
| Cassandra processes reads. |
| |
| Complete each table by adding any additional attributes identified by |
| the query. If any of these additional attributes are the same for every |
| instance of the partition key, mark the column as static. |
| |
| Now that was a pretty quick description of a fairly involved process, so |
| it will be worthwhile to work through a detailed example. First, |
| let’s introduce a notation that you can use to represent logical |
| models. |
| |
| Several individuals within the Cassandra community have proposed |
| notations for capturing data models in diagrammatic form. This document |
| uses a notation popularized by Artem Chebotko which provides a simple, |
| informative way to visualize the relationships between queries and |
| tables in your designs. This figure shows the Chebotko notation for a |
| logical data model. |
| |
| .. image:: images/data_modeling_chebotko_logical.png |
| |
| Each table is shown with its title and a list of columns. Primary key |
| columns are identified via symbols such as **K** for partition key |
| columns and **C**\ ↑ or **C**\ ↓ to represent clustering columns. Lines |
| are shown entering tables or between tables to indicate the queries that |
| each table is designed to support. |
| |
| Hotel Logical Data Model |
| ------------------------ |
| |
| The figure below shows a Chebotko logical data model for the queries |
| involving hotels, points of interest, rooms, and amenities. One thing you'll |
| notice immediately is that the Cassandra design doesn’t include dedicated |
| tables for rooms or amenities, as you had in the relational design. This |
| is because the workflow didn’t identify any queries requiring this |
| direct access. |
| |
| .. image:: images/data_modeling_hotel_logical.png |
| |
| Let’s explore the details of each of these tables. |
| |
| The first query Q1 is to find hotels near a point of interest, so you’ll |
| call this table ``hotels_by_poi``. Searching by a named point of |
| interest is a clue that the point of interest should be a part |
| of the primary key. Let’s reference the point of interest by name, |
| because according to the workflow that is how users will start their |
| search. |
| |
| You’ll note that you certainly could have more than one hotel near a |
| given point of interest, so you’ll need another component in the primary |
| key in order to make sure you have a unique partition for each hotel. So |
| you add the hotel key as a clustering column. |
| |
| An important consideration in designing your table’s primary key is |
| making sure that it defines a unique data element. Otherwise you run the |
| risk of accidentally overwriting data. |
| |
| Now for the second query (Q2), you’ll need a table to get information |
| about a specific hotel. One approach would have been to put all of the |
| attributes of a hotel in the ``hotels_by_poi`` table, but you added |
| only those attributes that were required by the application workflow. |
| |
| From the workflow diagram, you know that the ``hotels_by_poi`` table is |
| used to display a list of hotels with basic information on each hotel, |
| and the application knows the unique identifiers of the hotels returned. |
| When the user selects a hotel to view details, you can then use Q2, which |
| is used to obtain details about the hotel. Because you already have the |
| ``hotel_id`` from Q1, you use that as a reference to the hotel you’re |
| looking for. Therefore the second table is just called ``hotels``. |
| |
| Another option would have been to store a set of ``poi_names`` in the |
| hotels table. This is an equally valid approach. You’ll learn through |
| experience which approach is best for your application. |
| |
| Q3 is just a reverse of Q1—looking for points of interest near a hotel, |
| rather than hotels near a point of interest. This time, however, you need |
| to access the details of each point of interest, as represented by the |
| ``pois_by_hotel`` table. As previously, you add the point of |
| interest name as a clustering key to guarantee uniqueness. |
| |
| At this point, let’s now consider how to support query Q4 to help the |
| user find available rooms at a selected hotel for the nights they are |
| interested in staying. Note that this query involves both a start date |
| and an end date. Because you’re querying over a range instead of a single |
| date, you know that you’ll need to use the date as a clustering key. |
| Use the ``hotel_id`` as a primary key to group room data for each hotel |
| on a single partition, which should help searches be super fast. Let’s |
| call this the ``available_rooms_by_hotel_date`` table. |
| |
| To support searching over a range, use :ref:`clustering columns |
| <clustering-columns>` to store |
| attributes that you need to access in a range query. Remember that the |
| order of the clustering columns is important. |
| |
| The design of the ``available_rooms_by_hotel_date`` table is an instance |
| of the **wide partition** pattern. This |
| pattern is sometimes called the **wide row** pattern when discussing |
| databases that support similar models, but wide partition is a more |
| accurate description from a Cassandra perspective. The essence of the |
| pattern is to group multiple related rows in a partition in order to |
| support fast access to multiple rows within the partition in a single |
| query. |
| |
| In order to round out the shopping portion of the data model, add the |
| ``amenities_by_room`` table to support Q5. This will allow users to |
| view the amenities of one of the rooms that is available for the desired |
| stay dates. |
| |
| Reservation Logical Data Model |
| ------------------------------ |
| |
| Now let's switch gears to look at the reservation queries. The figure |
| shows a logical data model for reservations. You’ll notice that these |
| tables represent a denormalized design; the same data appears in |
| multiple tables, with differing keys. |
| |
| .. image:: images/data_modeling_reservation_logical.png |
| |
| In order to satisfy Q6, the ``reservations_by_guest`` table can be used |
| to look up the reservation by guest name. You could envision query Q7 |
| being used on behalf of a guest on a self-serve website or a call center |
| agent trying to assist the guest. Because the guest name might not be |
| unique, you include the guest ID here as a clustering column as well. |
| |
| Q8 and Q9 in particular help to remind you to create queries |
| that support various stakeholders of the application, not just customers |
| but staff as well, and perhaps even the analytics team, suppliers, and so |
| on. |
| |
| The hotel staff might wish to see a record of upcoming reservations by |
| date in order to get insight into how the hotel is performing, such as |
| what dates the hotel is sold out or undersold. Q8 supports the retrieval |
| of reservations for a given hotel by date. |
| |
| Finally, you create a ``guests`` table. This provides a single |
| location that used to store guest information. In this case, you specify a |
| separate unique identifier for guest records, as it is not uncommon |
| for guests to have the same name. In many organizations, a customer |
| database such as the ``guests`` table would be part of a separate |
| customer management application, which is why other guest |
| access patterns were omitted from the example. |
| |
| |
| Patterns and Anti-Patterns |
| -------------------------- |
| |
| As with other types of software design, there are some well-known |
| patterns and anti-patterns for data modeling in Cassandra. You’ve already |
| used one of the most common patterns in this hotel model—the wide |
| partition pattern. |
| |
| The **time series** pattern is an extension of the wide partition |
| pattern. In this pattern, a series of measurements at specific time |
| intervals are stored in a wide partition, where the measurement time is |
| used as part of the partition key. This pattern is frequently used in |
| domains including business analysis, sensor data management, and |
| scientific experiments. |
| |
| The time series pattern is also useful for data other than measurements. |
| Consider the example of a banking application. You could store each |
| customer’s balance in a row, but that might lead to a lot of read and |
| write contention as various customers check their balance or make |
| transactions. You’d probably be tempted to wrap a transaction around |
| writes just to protect the balance from being updated in error. In |
| contrast, a time series–style design would store each transaction as a |
| timestamped row and leave the work of calculating the current balance to |
| the application. |
| |
| One design trap that many new users fall into is attempting to use |
| Cassandra as a queue. Each item in the queue is stored with a timestamp |
| in a wide partition. Items are appended to the end of the queue and read |
| from the front, being deleted after they are read. This is a design that |
| seems attractive, especially given its apparent similarity to the time |
| series pattern. The problem with this approach is that the deleted items |
| are now :ref:`tombstones <asynch-deletes>` that Cassandra must scan past |
| in order to read from the front of the queue. Over time, a growing number |
| of tombstones begins to degrade read performance. |
| |
| The queue anti-pattern serves as a reminder that any design that relies |
| on the deletion of data is potentially a poorly performing design. |
| |
| *Material adapted from Cassandra, The Definitive Guide. Published by |
| O'Reilly Media, Inc. Copyright © 2020 Jeff Carpenter, Eben Hewitt. |
| All rights reserved. Used with permission.* |