sql - Entity Relationship Diagram for Hotel - Stack Overflow
Modeling is about mapping entities and relationships of the world into the The Relational Model uses relations to represent entities . A foreign key may involve an entity that has itself a foreign key, as . Hotel near studio? Two types of. Translation of a (Normal Form) ER Diagram to a RDB. ❑ A Normal Form for ER . Q: Are the concepts of identifier of entity type and primary key of relation of relational model the .. choosing primary keys and foreign keys. - defining indexes. ER diagram for library management system to design a library management system. Click the button to make necessary changes and export it. Related Diagrams. (12). E-R Diagram for Hotel management system. (9).
So person-owns-pet would be 0-or-MANY. In general we put the number of possibilities on either end of a line and say "possibility-to-possibility" or "possibility: The lines are really just foreign keys from the table to participant entity tables. A person can be married to another person; a relationship instance of that is also an associative entity instance of a marriage. Presumably it's how this style shows n-ary relationships.
The actual relationship for such a label is represented by a projection of the associative entity type table. Other conventions Some methods restrict possibilities to particular choices. Sometimes "1" means "0-or-1". Some methods distinguish 0-or-1 participation in a relationship via a relationship row being absent or present vs via an obligatory but nullable foreign key.
Some methods allow relationships with more than two participants. Then you just draw another line from the label to an entity. Some methods label the far end of a line from an entity with its cardinality. Doesn't handle n-ary relationships unless you encode them as associative entitites so all "relationships" are binary.
Some methods have symbols for labels.
Some methods have unlabelled lines which are just foreign keys. There happens to be a good article in the wiki. Foreign keys We do not need foreign keys to know what the relationships mean or to update or query a database.
We query by combining relationships and conditions into other relationships while the DBMS builds a corresponding table expression and calculates its value.
E-R Diagram of Library Management System ( Entity Relationship Diagram)
Hotel domain logical model Let's explore the details of each of these tables. Let's reference the point of interest by name, because according to our workflow that is how our users will start their search. So we add the hotel key as a clustering column. Note Make Your Primary Keys Unique 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.
When the user selects a hotel to view details, we can then use Q2, which is used to obtain details about the hotel. Therefore our second table is just called hotels. This is an equally valid approach. This helps to minimize coupling between different entity types. This may prove especially helpful if you are using a microservice architectural style for your application, in which there are separate services responsible for each entity type.
For the purposes of this book, however, we'll use mostly text attributes as identifiers, to keep our samples simple and readable. For example, a common convention in the hospitality industry is to reference properties by short codes like "AZ" or "NY". Q3 is just a reverse of Q1—looking for points of interest near a hotel, rather than hotels near a point of interest.
As we have done previously, we 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 our 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 we're querying over a range instead of a single date, we know that we'll need to use the date as a clustering key. Note Searching Over a Range Use clustering columns to store attributes that you need to access in a range query.
Remember that the order of the clustering columns is important.
We'll learn more about range queries in Chapter 9. This will allow our user to view the amenities of one of the rooms that is available for the desired stay dates. Reservation Logical Data Model Now we switch gears to look at the reservation queries. You'll notice that these tables represent a denormalized design; the same data appears in multiple tables, with differing keys. We 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, we include the guest ID here as a clustering column as well. 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, we create a guests table. You'll notice that it has similar attributes to our user table from Chapter 4.
This provides a single location that we can use to store our guests. In this case, we specify a separate unique identifier for our guest records, as it is not uncommon for guests to have the same name. In many organizations, a customer database such as our guests table would be part of a separate customer management application, which is why we've omitted other guest access patterns from our example.
Note Design Queries for All Stakeholders Q8 and Q9 in particular help to remind us that we need to create queries that support various stakeholders of our application, not just customers but staff as well, and perhaps even the analytics team, suppliers, and so on.
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. We've already used one of the most common patterns in our hotel model—the wide row.
The time series pattern is an extension of the wide row pattern. In this pattern, a series of measurements at specific time intervals are stored in a wide row, 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. We 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.
We'd probably be tempted to wrap a transaction around our 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 row. 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.
Entity–relationship model - Wikipedia
The problem with this approach is that the deleted items are now tombstones 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.
Physical Data Modeling Once we have a logical data model defined, creating the physical model is a relatively simple process. We walk through each of our logical model tables, assigning types to each item.
We can use any of the types we covered in Chapter 4including the basic types, collections, and user-defined types.
We may identify additional user-defined types that can be created to simplify our design. After we've assigned our data types, we analyze our model by performing size calculations and testing out how the model works. We may make some adjustments based on our findings. Once again we'll cover the data modeling process in more detail by working through our example.
Before we get started, let's look at a few additions to the Chebotko notation for physical data models. Chebotko Physical Diagrams To draw physical models, we need to be able to add the typing information for each column. The figure includes a designation of the keyspace containing each table and visual cues for columns represented using collections and user-defined types.
We also note the designation of static columns and secondary index columns. There is no restriction on assigning these as part of a logical model, but they are typically more of a physical data modeling concern. Extending the Chebotko notation for physical data models Hotel Physical Data Model Now let's get to work on our physical model. First, we need keyspaces for our tables.
To keep the design relatively simple, we'll create a hotel keyspace to contain our tables for hotel and availability data, and a reservation keyspace to contain tables for reservation and guest data. In a real system, we might divide the tables across even more keyspaces in order to separate concerns. For our hotels table, we'll use Cassandra's text type to represent the hotel's id. For the address, we'll use the address type that we created in Chapter 4.
We use the text type to represent the phone number, as there is considerable variance in the formatting of numbers between countries. As we work to create physical representations of various tables in our logical hotel data model, we use the same approach.
Hotel physical model Note that we have also included the address type in our design. It is designated with an asterisk to denote that it is a user-defined type, and has no primary key columns identified. Note Taking Advantage of User-Defined Types It is often helpful to make use of user-defined types to help reduce duplication of non-primary key columns, as we have done with the address user-defined type.
This can reduce complexity in the design. Remember that the scope of a UDT is the keyspace in which it is defined. To use address in the reservation keyspace we're about to design, we'll have to declare it again.
This is just one of the many trade-offs we have to make in data model design. Reservation Physical Data Model Now, let's turn our attention to the reservation tables in our design.
Remember that our logical model contained three denormalized tables to support queries for reservations by confirmation number, guest, and hotel and date. As we work to implement these different designs, we'll want to consider whether to manage the denormalization manually or use Cassandra's materialized view capability. We'll discuss the reasoning behind this design choice momentarily. Creating indexes on columns with high cardinality tends to result in poor performance, because most or all of the nodes in the ring need are queried.
Materialized views address this problem by storing preconfigured views that support queries on additional columns which are not part of the original clustering key. Materialized views simplify application development: Materialized views incur a small performance impact on writes in order to maintain this consistency.
However, materialized views demonstrate more efficient performance compared to managing denormalized tables in application clients.
Internally, materialized view updates are implemented using batching, which we will discuss in Chapter 9. Similar to secondary indexes, materialized views can be created on existing tables.
This restriction keeps Cassandra from collapsing multiple rows in the base table into a single row in the materialized view, which would greatly increase the complexity of managing updates. The grouping of the primary key columns uses the same syntax as an ordinary table.
The most common usage is to place the additional column first as the partition key, followed by the base table primary key columns, used as clustering columns for purposes of the materialized view.
Note Enhanced Materialized View Capabilities The initial implementation of materialized views in the 3. If you're interested in these features, track the JIRA issues to see when they will be included in a release. Now that we have a better understanding of the design and use of materialized views, we can revisit the prior decision made for the reservation physical design.
However, because we cannot at least in early 3. Both designs are acceptable, but this should give some insight into the trade-offs you'll want to consider in selecting which of several denormalized table designs to use as the base table. Evaluating and Refining Once we've created our physical model, there are some steps we'll want to take to evaluate and refine our table designs to help ensure optimal performance.
Calculating Partition Size The first thing that we want to look for is whether our tables will have partitions that will be overly large, or to put it another way, partitions that are too wide. Partition size is measured by the number of cells values that are stored in the partition. Cassandra's hard limit is 2 billion cells per partition, but we'll likely run into performance issues before reaching that limit. In order to calculate the size of our partitions, we use the following formula: The number of values per row is defined as the number of columns Nc minus the number of primary key columns Npk and static columns Ns.
The number of columns tends to be relatively static, although as we have seen it is quite possible to alter tables at runtime.
For this reason, a primary driver of partition size is the number of rows in the partition. This is a key factor that you must consider in determining whether a partition has the potential to get too large. Two billion values sounds like a lot, but in a sensor system where tens or hundreds of values are measured every millisecond, the number of values starts to add up pretty fast. Let's take a look at one of our tables to analyze the partition size.
Plugging these values into our formula, we get: We still need to determine a number of rows. To do this, we make some estimates based on the application we're designing. Our table is storing a record for each room, in each of our hotels, for every night. Let's assume that our system will be used to store two years of inventory at a time, and there are 5, hotels in our system, with an average of rooms in each hotel.
Since there is a partition for each hotel, our estimated number of rows per partition is as follows: We still might want to look at breaking up this large partition, which we'll do shortly. Note Estimate for the Worst Case When performing sizing calculations, it is tempting to assume the nominal or average case for variables such as the number of rows.
Consider calculating the worst case as well, as these sorts of predictions have a way of coming true in successful systems. Calculating Size on Disk In addition to calculating the size of our partition, it is also an excellent idea for us to estimate the amount of disk space that will be required for each table we plan to store in the cluster. In order to determine the size, we use the following formula to determine the size St of a partition: Let's take a look at the notation first: The term tavg refers to the average number of bytes of metadata stored per cell, such as timestamps.
It is typical to use an estimate of 8 bytes for this value. We recognize the number of rows Nr and number of values Nv from our previous calculations. The sizeOf function refers to the size in bytes of the CQL data type of each referenced column. The first term asks us to sum the size of the partition key columns. Assuming our hotel identifiers are simple 5-character codes, we have a 5-byte value, so the sum of our partition key column sizes is 5 bytes.