Logical Enigma
AWS Notes

Databases

AWS Databases

  • Relational Database Service RDS
    • Oracle, SQL Server, MySQL, PostgreSQL, Aurora, MariaDB
    • Multi-AZ - for disaster recovery - exact copy in another AZ, AWS will change the endpoint during failover - no intervention (not available for Aurora b/c it’s fault tolerant by design). no charge for data transfer. Can use Reserved DB Instances
    • Read Replicas - for performance - async replication from primary. RR can be promoted to standalone DB (breaks replication)
    • Not for DR
    • Must have automatic backups turned on
    • Up to 5 read replicas (but an of RR of RR)
    • Each RR has its own DNS endpoint
    • RR can be Multi-AZ
    • Can be in a second region
    • Not serverless (except Aurora)
    • Runs on VMs - can’t log in - Amazon responsible for patching
    • Automated Backups are enabled by default
    • Point in time recovery w/in retention period (1-35 days)
    • Full daily snapshots plus transaction logs throughout day
    • May experience latency during window
    • Deleted with the RDS db
    • DB Snapshots are done manually - stored even after you delete original RDS db
    • Restore an instance to a new RDS instance, which means new endpoint (change your apps)
    • Encryption at rest - KMS - backups, etc also
    • RDS for SQL Server max is 16TB when using Provisioned IPS and General Purpose (SSD) storage types
  • Non relational NoSQL DynamoDB
    • Collection = table; Document = row; Key Value Pairs = fields
  • Data Warehouse - Redshift
    • OLTP - online transaction processing
    • OLAP - online analytics processing
  • ElastiCache - in memory cache
    • Redis
    • memcached

DynamoDB

  • SSD storage - single digit millisecond latency - document and k-v data models
  • Across 3 Geo-distinct data
  • Eventual consistent reads (default = ~1sec) vs. strongly consistent reads
  • Accelerator - DAX : in-memory cache. Can write-through cache
    • Fully managed, HA. 10x performance improvement
    • Request time down to microseconds
  • Transactions -
    • Multiple “all or nothing” ops
    • 2 underlying reads/write - prepare/commit
    • Up to 25 items or 4MB of data
  • On-Demand Capacity - pay per request pricing (balance cost and performance) - pay more per request than provisioned
  • On-Demand Backup and Restore - full backup at any time, zero impact on perf or availability. Consistent w/in seconds and retained until deleted
  • Point in Time Recovery (PITR) - protect against accidental writes or deletes, up to 35 days, incremental backups, not enabled by default. Latest restorable = 5 mins in past
  • Stream - time-ordered sequence of item-level changes - stored 24 hrs. Inserts, updates, deletes. Combine w/ lambda’s to simulate stored procedures
  • Global Tables - managed multi-master, multi-region replication - based on streams, multi-region redundancy for DR or HA. No application requires. Replication latency under one second.
  • Database Migration Service (target, not source)
  • Encrypted at rest using KMS. Site to Site VPN, Direct Connect (DX), IAM, Fine-grained access, VPC endpoints, Cloudwatch and CloudTrail
  • Strongly Consistent Reads can be used but may have higher latency
  • DynamoDB allows for the storage of large text and binary objects, but there is a limit of 400 KB.

Redshift

  • Fully managed petabyte-scale data warehouse
  • no commitments/upfront costs - 0.25/hr -> 1K per TB per year
  • Single Node (160Gb)
  • Multi-Node
    • Leader Node - manages client conx and receives queries
    • Compute Node - store data and perform queries and computations (up to 128 compute nodes)
  • Advanced Compression - columnar data stores can be compressed much more than row-based. doesn’t require materialized views, etc
  • Massively parallel processing
  • Backups - enabled by default w/ a 1 day retention (max 35)
    • attempts to maintain at least 3 copies of data (original, replica on compute nodes, and backup in s3)
    • async replicate your snapshots to s3 in another region for DR
  • Charges - compute node only - 1 unit per node per hour + backup + data transfer w/in VPC
  • Security - transit + rest. KMS but can use HSM
  • Currently only available in 1 AZ
  • Can restore snapshots in another AZ

Aurora

  • MySQL & PostgreSQL compatible
  • Up to 5x perf over MySQL; 3x over PGSQL
  • 10GB, scale in 10GB increments to 64TB
  • Compute can scale to 32vcpu and 244 gb memory
  • 2 copies of data in each AZ, minimum 3 az = 6 copies of your data
  • Transparently handle the loss of up to 2 copies of data w/o affecting write avail. up to 3 copies w/o affecting read availability
  • Self healing - data blocks and disks are scanned for errors and repaired automatically
  • Aurora Replicas - max 15 - in region replications
  • Mysql Read Replicas - max 5
  • PGSQL read Replicas - max 1
  • Backups are always enabled. Snapshots can be shared w/ other AWS accounts.
  • Aurora Serverless - automatically starts/stops/scales - for infrequent, intermittent or unpredictable workloads

ElastiCache

  • In memory cache in the cloud - increase DB and web application performance
  • memcached - simple
  • Redis - pub sub, complex data types, backup and restores, multi-az

Database Migration Service (DMS)

  • migrate to cloud, between on-premises, combos
  • Server that runs replication software - source/target
  • Will create tables and primary keys, but can pre-create manually or use AWS Schema Conversion Tool (SCT) needed for heterogeneous
  • Supports homogenous / heterogeneous migrations
  • Sources - usual DBs, Azure SQL, RDS, S3
  • Targets - usual DBs, RDS, Redshift, DynamoDB, S3, Elasticsearch, Kinesis data streams, DocumentDB

Caching Services

  • CloudFront
  • API Gateway
  • ElastiCache - Redis / memcached
  • DynamoDB Application Accelerator - DAX

Amazon EMR

  • Elastic Map Reduce
  • Spark, Hive, HBase, Flink, Hudi, Presto
  • Petabyte-scale analysis at 1/2 cost of traditional on-prem and 3x faster than Apache Spark
  • Cluster - nodes w/ roles
    • Master node - status of tasks & monitors health - every cluster has a master node
    • Core node - runs tasks and stores data in Hadoop Distributed File System (HDFS) - multi-node clusters have at least one core node
    • Task node - only runs task and does NOT store data in HDFS - optional
  • Log files stored on master node at /mnt/var/log
    • configure to archive logs to S3 at 5-minute intervals in case of normal shutdown or error
    • must be done when first set up cluster

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