Databases forms the crucial foundation of data-driven decisions in every sphere of business and society today. As per IDC estimates, global data volumes are expected to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025!
To leverage this exponential growth of data, having an efficient and flexible database management system (DBMS) is non-negotiable. Industry surveys show that close to 80% of enterprises are accelerating their DBMS adoption.
This widespread reliance on databases makes mastering DBMS concepts like attributes critical not just for developers but also business analysts, data engineers and database administrators.
Without understanding how to analyze and incorporate different attribute types into your data models, you risk suboptimal database schema design and poor application performance. Prototyping a simplistic structure based on assumptions rather than actual query and storage patterns can backfire badly. Re-engineering deployed schemas with large datasets can be painful and highly disruptive.
So before you design your next application or analytics database, let‘s dive deeper into the most essential DBMS construct – the attribute!
Revisiting Database Attributes
Attribute refers to a characteristic property that helps define and constrain an entity in a database system. For instance in an employee database, first_name, last_name, salary, join_date etc can be potential attributes.
Each attribute has a set of defined properties like data type, length, nullable status etc. based on business rules and domain requirements.
So why learn these database concepts? What crucial role do attributes play in application development?
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Attributes enable business logic modeling by capturing essence of entities. Poor attributes lead to faulty analysis.
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Storage engines leverage attribute metadata for memory allocation so sizing matters.
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Constraints and relationships depend on attributes to enforce data integrity.
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Query performance hinges on attributes filtration and aggregation capability.
In summary, choosing the right attributes and their types is key to building an efficient analytical data platform. Misconfigured attributes entail painful schema rework down the road.
While basic attribute types like text, integer, float are well understood, some of the more complex and specialized genre often cause confusion. Let‘s demystify them one by one!
Attribute Types by Composition
1. Simple Attributes
Attributes representing atomic values without further sub-types are known as simple attributes. For instance, first_name, revenue, age etc. fall under this category. Simple usually means native data types like numeric, string, boolean or temporal.
Examples: salary, movie_name, age
In RDBMS, simple attributes manifest as standalone columns with native data types and constraints. In NoSQL models like key-value or document databases, these become singular data dictionary elements.
2. Composite Attributes
To represent more complex characteristics, multiple inter-related simple attributes can be grouped together as composites. For instance, combination of street, city, state and country attributes gives a more meaningful address attribute.
Other examples include name split across first_name, middle_name, last_name and date of birth using day, month and year fields.
Composite attributes enforce better organization for related sub-elements and also reduce data redundancy. However element-level querying may get complex necessitating application-side joins.
3. Derived Attributes
For certain use cases like analytics, storing and maintaining frequently changing operational attributes is unnecessary. For example, while order_date needs storage, age_of_order_in_days can be calculated on the fly using date arithmetic rather than duplicate storage. Such attributes derived from other base attributes are known as derived attributes.
Other common derived attributes include aggregates like avg_sale_amount, max_bill_amount etc. as well as status indicators like age_bracket using case logic on base age value.
Derived attributes minimize storage needs and ensure single source of truth. However runtime computation can get expensive affecting query performance. Caching frequently accessed derived attributes using materialized views is one optimization approach.
Attribute Types by Value Cardinality
4. Single Valued Attributes
Attributes that can take only one value per entity instance are termed single valued attributes. For example, person_dob or insurance_policy_number allows only one birthdate or policy ID per person.
Uniqueness constraints automatically make an attribute single valued like email_id or social_security_number.
Examples: transaction_id, policy_number, email_address
Single valued attributes simplify application modeling and avoid insertion anomalies through normalization. But real world exceptions like existing users changing their email address or system generated IDs getting reused after deletions make it trickier.
5. Multi-Valued Attributes
In contrast, attributes that need to record multiple values per entity require multi-valued data types.
For instance, storing languages known or phone contact numbers for a person entity necessitates multi-valued attributes for full information capture.
Other examples include student course registrations, doctor qualifications and car features.
Multi-valued attributes enable schemaless data models to handle iteratively growing attributes like tags, bookmarks, playlist items etc. Array, list or associative table storage helps query individual elements fast too.
6. Null-Valued Attributes
Missing information is ubiquitous in real world data for a variety of reasons like unfinished input, partial migrations, non-disclosure etc. Blank values create confusion on intent – whether its applicable or unknown. Null values explicitly indicate inapplicability of an attribute for a given record.
For instance, students may omit secondary email addresses or contacts. Patient middle names may not be provided or doctors may refrain from noting pre-existing conditions due to liability reasons. Null denotes absence of value irrespective of underlying cause.
Examples: middle_name NULL, previous_employer NULL
NULL handling needs special operators in query predicates and joins. Constraints also require tweaks to allow NULLs where applicable.
Misuse of nulls due to poor design often introduces data anomalies affecting integrity and accuracy of applications – so handle with care!
Advanced Attribute Types
Beyond the common attribute varieties discussed above, a few specialty genres also exist:
7. Virtual Attributes
Computation intensive derived attributes that only materialize dynamically fall under the virtual attribute category. No storage overhead is incurred with such attributes unlike persisted materialized views.
For example, in data warehousing, aggregation of raw facts happens inline rather than storing all permutations. Temporary runtime values like row_number() over a window also qualify as virtual attributes.
8. Stored Attributes
In contrast, stored attributes leverage additional storage structures to enable direct access and filtering by business needs. Clustered indexes in RDBMS making the index and data pages consistent or document DB pointers fall under this category.
Stored attributes speed up read performance trading off higher memory and I/O overheads.
9. Nullable Attributes
By default, database columns allow NULL values unless explicitly constrained as NOT NULL. Such attributes are classified as nullable attributes. Constraints checking for NULL input are only imposed during backend transaction processing and do not restrict adhoc interactive analyses allowing NULLs.
DBMS Attribute Implementation Considerations
Based on the storage system, transaction loads and access patterns attributes need specialized handling:
1. Indexing: Highly filtered attributes make good candidates for database indexes for faster lookups. Multi-column indexes enable combined filters. Columns with duplicate values require techniques like bitmap indexes.
2. Partitioning: Dividing tables horizontally or vertically on partition key columns with date, geo attributes speeds up staging and archival by eliminating full table scans. Query performance also sees order of magnitude gains from partition pruning.
3. Clustering: Columnar storage in data warehouses relies on clustering most frequently accessed attributes to minimize I/O. Clustered indexes pin compressed data to btree leaf nodes in RDBMS for similar efficiency.
4. Compression: Dictionary encoding translates repeating attribute values to small integers saving storage and memory bandwidth. LZW style textual substitution compresses verbose strings losslessly.
5. Caching: In-memory caches leverage hottest attributes to serve majority of transactions without hitting storage layer using techniques like SSD lookup tables.
These are just some of the considerations for tuning DBMS systems based on the relevance and usage patterns of attributes.
Without meticulous attribute planning, systems end up severely underutilizing expensive infrastructure leading to subpar ROI on database investments!
Real World DBMS Attribute Modeling
While the theory on various attribute types gives a great conceptual foundation, nothing beats examining some real world examples from different domains:
1. User Management
Typical user account systems leverage a combination of simple single value attributes like username, password along with multi-value attributes such as email_ids, phone_numbers and null able values like middle_names.
Virtual attributes like account_status facilitate soft deletes while last_login_times get periodically persisted for activity tracking.
2. Order Processing
Ecommerce order systems heavily utilize deterministic and internally generated ids like order numbers, transaction ids as single value attributes. Item selections and comments make good contenders for multi-value attributes.
Shipping and billing blocks often group as composite address attributes with null able fields like floor number. And no order system is complete without a basket of time-series derived attributes on shipment, delivery, fulfillment status as well as calculated price, tax and discount elements!
3. Healthcare Information
Electronic health records have an especially high bar for attribute quality control due to extensive regulatory compliance requirements across geographies for data sensitivity, accuracy, retention and encryption.
Patient Ids and social security numbers get masked while composite names split by prefix, suffix and degrees to maintain referential integrity across systems. Nullable values are allowed only under strict validation rules for incomplete input.
Multi-value repeating attributes capture diagnosis history and lab results across episodes while allergies, medications, conditions get specially flagged derived attributes on severity, acuity and state. External feeds, pre-processing and master data management ensures integrity of healthcare attributes.
This should give you a flavor of real world considerations while modeling DBMS attributes!
Query Performance and Constraints
We have discussed storage structures and access patterns for different attribute types. But what about query runtime evaluation?
Typically, selective attributes prefixed to query predicates via WHERE and JOIN clauses provide the best optimizations to limit resultsets earlier using indexes and partitions. In OLAP workloads, star schema dimensional attributes aid aggregation performance greatly.
However badly designed predicates and subqueries on unreliable derived attributes and nullable columns often yield wrong results with huge scans. Nested attributes and bulky composites also drag down performance unless denormalized into summary reporting tables.
Referential and data integrity also revolve around attributes. Foreign keys need precisely matched attributes across parent-child tables for joins to work. Data cleansing routines lean on pattern matching attributes using constraints and triggers to flag anomalies.
As a tip, constrain attributes based on business rules but inspect resulting queries before blindly applying blankets NOT NULLs and UNIQUEs everywhere! Also prefer vertical partitioning for unrelated attributes instead of horizontal schema redundancy.
Schema Migration and Modernization
With agile DevOps practices, attributes require frequent enrichment from operational systems or analytics needs. Alter statements help ease one or two columns but whole compose restructures need more intricate handling not to destabilize systems.
Gradual migration works better, for instance, first snapshot reporting clusters separately, then switch reads selectively and finally reconnect transactions last. Observe workload patterns before refactoring complex data types and query flows.
Scale-out NoSQL models allow flexible expansion of embedded attributes with geo-distribution sustaining uptime. Polyglot persistence allows best-of-breed storage for each attribute based on access type – hot columns in memory, archival attributes on cloud object stores and so on.
Final Thoughts
Database design is often an afterthought by developers rushing to meet features and release deadlines. But even the most elegant code logic fails without a robust and extensible data model foundation!
Attributes form the basic building blocks of this model so ensure you learn and apply the critical concepts covered in this guide before your next database project.
To recap, the key facets that need careful attribute planning are:
✔️Composition: Structure attributes logically into simple or nested composites
✔️Cardinality: Determine single vs multi-value nature wisely
✔️Derivation: Minimize storage with computed attributes
✔️Metadata: Define precision, scale, truncation, case-sensitivity, charset etc. appropriately
✔️Nullability: Set null/not null constraints judiciously
✔️Data Types: Pick optimal data types to avoid overflow or truncation
✔️Referential Integrity: Ensure joins via foreign key, partitioning alignments
✔️Access Patterns: Index, cluster attributes matched to query filters
By mastering these database concepts, you can become a truly skilled DBMS practitioner able to build efficient analytical solutions. Help stakeholders in your organization make smarter decisions leveraging the foundation of versatile attribute modeling!
