Designing Data Intensive Applications - Chapters 1 through 3

Data Intensive Application Data as primary challenge Quantity of Data / Complexity of Data / Speed of Data

Lots of Buzzowords like AWS / NOSQL / etc. Choosing from these means understanding tradeoffs Book details how to navigate and select from all these options


Reliabilitiy Scalability Maintability

Databases / Caches / Search Index / Stream processing / Batch Processsing SP is filtering data by key words Batch Processing is crunching large amounts of data

These can all be considered Data Systems and they can interact with each other to help a overall application

Reliability User errors or Mistakes are handled Performs as user expects Performance meets required case Prevents unauthorized access Functional / Non-Functional Requirements*

Hardware Errors Problems with hardware can arise, in sources that want to be highly avaiable RAID / Redudancy is added. But this increases rates of hardware failure Common for AWS to be down Software Errors Cause bigger system errors than hardware errors No real solution, although testing, process isolation, and crash/restart can help Measuring / Monitoring / Analyizing system behavior in production Test at all levels -> Unit / Integration / Manual / Regression Roll back capabilities Generally very important to reduce loss of money (Or catatrophic nuclear failure) Can cut corners but need to be conscious of risks SCALABILITY What will the service look like in the future? What happens if data expands? How is it handled?

Discover what current load is, then look for ways to optimize* Twitter example with updating timeline tweets at WRITE vs. VIEW

Once load is found, examine what happens when it increases (Measure Throughput or time)

latency vs. response time (time until request finishes vs. time until user sees change) Median is better than average for estimating time data Percentiles can also tell a story -> higher percentile (tail latency) usually serve bigger data Service Level Objectives (SLOs) and Service Level Aggrements (Contracts the define expected performance) Queueing delays and waiting for 1 slow application request can stall everything else

How to improve architecture scalability Usually have to redesign it as it scales (Load 1 design does not work for Load 10 design) Vertical scaling vs. Horizontal Scaling (shared-nothing / scaling out) Elastic systems for unpredictable traffic There is no magic key, need to evaluate types of distributed systems MAINTAINABILITY People dislike working with legacy software, but it’s important to reduce pain Operability / Simplicity / Evolvability

Operability involves making life easier for operators (People who monitor / keep software up to date) Data systems that have good visibility for this is preferred Good Documentation / default behavior / Avoid dependency on 1 machine(!!!) Simplicity OOP / consistent naming / Avoiding tangled dependencies


Data models are important part of applications Very difficult to master the intricacies of one data model (There’s 20+ books on relational modeling) This chapter will go over Relational / Document / some graph based models

RELATIONAL VS. DOCUMENT MODEL SQL as most popular relational DB model Developed in 1970s and was overall best among competitors, also has good general use cases NOSQL rose to reduce difficulties from SQL Scalability, simpler to code than SQL, specialized query operations, Open Source Likely that NOSQL co-exist with SQL Object-Relational Mismatch (Tables don’t map to objects, code needs to be made for this) JSON representations don’t have this problem (However, it still has other issues) IDs. vs Strings (IDs are useful because they remain consistent / avoid ambiguity, human language changes)

Many to One vs. One to Many
	Using Joins on JSON is hard, maybe can reference objects instead of strings

DOCUMENT MODEL In 1970s, IBM’s IMS was a hierarchical / tree model that had many similarities with JSON Eventually faded away when SQL grew Network MODEL / CODASYL -> Struggled if the path for data wasn’t known Relational DB was another solution -> Allows data without lookups / paths… Query optimizations

Code Complexity Document good at One to Many (Lack of joins means Many to Many is difficult)

Schema Flexibility No schema enforced means any document can contain anything Documents have a schema-on-read, relational has schema-on-write that’s enforced What if an application wants to change the format of data? Document needs to write new documents with new fields Relational has to use ALTER query

Documents are great if data stored can have different fields, Schema-enforced relational DB would be hard here

Data Locality All the data is loaded at once (Faster if all data is being used) Convergence of Document / Relational XML is allowing querying / indexing…. PostgreSQL is allowing JSON support.. Others soon to follow?

QUERY LANGUAGES Imperative -> most common Declarative programming offers super fast code by telling computer what type of data to get Map Reduce Querying -> hybrid (MONGODB)

GRAPH-LIKE MODELS Document good for ONE-to-MANY or No-relationships… Relational good for many-to-many When the many-to-many is too big -> Use graphs Social Network // Web graph // Rail / road network

Property Graph Model (Neo4j, Titan, InfiniteGraph) Vertex has ID / outgoing edges / incoming edges / properties (key-val pairs) Edge has ID / head(ends) / tail vertex (where edge starts) / label for relationship / properties

No restrictions for vertex connections // Can easily find incoming + outgoing edges for a vertex // Labels for relationships can store info on graph Has good evolvability due to flexible nature

Cypher query language for graphs

Triple-Store Model

Chapter 3 - Storing and Retrieving Data (Reads and Writes)

Simple method is to append to end of file. And search entire file when reading (slow)

Hash Index Mapping data at offsets so you can skip more of the file k1 -> offset 1, key2 -> offset 64 (better than searching entire file)

These files can be segmented to allow new files to be created when space runs out
	Compaction (Throwing away duplicate keys in log)
Details of implmentation
	Store using byte file
	Append special char to bytes that need to be deleted
	Crash recovery
		Store snapshots that can be reloaded
		Partially written records deleted
		Writer locks file
Benefits -> Appending is fast, crash recovery is simple
	removing old segments removes need of fragmentation
Limitations -> hash-table must fit in memory
	Range queries are difficult / slowish

SSTables and LSM-Trees Sorted String Table (SSTables) Instead of appending to end, keep in sorted order (Allows faster searching) How to maintain sorted order? B - Trees work. In-Memory works too using a tree structure like Red-Black / AVL 1. Insert in sorted order 2. Check if memtree is bigger than some capacity…Make new one if it is 3. on Read, search memtable, then check disk-segment, and older segments 4. Occasionally run merge / compaction in background to delete files/overwrite values Handling Crashing Memtable is lost if it crashes…Prevent loss by append every memtable write to a stack

LSM - Trees
	Process above is used in LevelDB and RocksDB
	Indexing structure called Log-Structured Merge-Tree
	Seem to be exact same as SSTables
	Optimizing -> Misses are expensive
		Fix this by using Bloom Filter(Approximates set contents)
	Strategies for compacting/merging
		Size-Tiered / Leveled Compaction. HBase(level)/Cassandra(does both)
		Newer / smaller SStables are merged into older/larger tables (Size-tiered)
		Key range is split into smaller SSTables and older data moved into levels
	LSM keep a cascade of SSTables that are merged in background. 
	Simple and Effective

B-Tree One of most common index References point to other References in a range of numbers Uses fixed blocks / Pages (4KB usually)

	During update -> Search ranges for the key
	During insert -> Search ranges until at the end, and add it (If size too big, split list in half)
		Then update parent
Can watch a youtube video to learn more

LSM-Tree vs. B-Tree LSM has less writes. B-Trees need to write at least twice (Once for WAL log in case of crash, Once on tree) Some storage engines write same page twice to prevent power outage partial write LSM rewrite during compaction / merging phase (Write amplification) Write heavy application will notice the difference LSM is compressed better, yielding smaller files on disk

Compaction can be expensive
Disk bandwidth expands bigger database gets
Compaction could possibly not keep up with the write requests

B-Trees provide consistently good performance. Each key exists in one place wheras LSM might have multiple copies
Transaction isolation can be done by locking ranges

OTHER STRUCTURES Primary key -> Uniquly identifies a row in SQL/relational Secondary index -> Important for joining data Heap file -> stores rows for indexing… Avoids duplicating data when multiple secondary indexes present Index referes to location Multiple Columns What if you want a key to have multiple values? Concatenated index -> rule is set to append X columsn when key/index is found Multiple-dimensional like below can’t be done in a B-Tree / LSM

	SELECT * FROM restaurants WHERE latitude > 51.4946 AND latitude < 51.5079
		AND longitude > -0.1162 AND longitude < -0.1004;

Ful-text-search / fuzzy indexes

In Memory Databases Don’t need to write to a hard disk, everything in memory Can use data models that are complicated to use with a hard disk

Anti-caching approach, a type of virtual memory can be used by evictng LRU cache and putting in disk

Transaction Processing vs. Analytics

Online Transaction Processing (OLTP) Standard CRUD applications, writing data into database and reading it. Blogs / Old Websites / Banking / etc. Online Analytic Processing(OLAP) -> What was revenue for Jan -> June… / Queries to improve business Reading large amounts of data and taking a small part of it Now, we are moving away from using SQL for both… Data Warehousing for OLTP

Data Warehousing Give the analytics people their own read-only database Improves security / isolates processing for the big OLTP data OLTP needs to be highly available and low latency ETL (Extract, Transform, Load) -> Moving OLTP data to OLAP Small businesses usually don’t know about OLAP, the small data works with pure relational Data warehouses can be optimized for analytics patterns indexing algorithms don’t help OLAP queries Data Warehouse is typically a relational database for the queries STAR SCHEMA -> WHERE/WHEN/HOW, one central table that branches off into these dimension tables** Data warehouses have very wide tables and fact tables have over hundreds of columns Dimension tables can also be wide OLTP databases are usually row-oriented