🏭🗒️Microsoft Fabric: Sharding [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 9-Feb-2024

 [Microsoft Fabric] Data Partitioning (aka Sharding)

• {definition} "a process where small chunks of the database are isolated and can be updated independently of other shards" [2]
• allows a logical database to be partitioned across multiple physical servers [1]
• each partition is referred to as a shard
• the largest tables are partitioned across multiple database servers [1]
• when operating on a record, the application must determine which shard will contain the data and then send the SQL to the appropriate server [1]
• partitioning is based on a Key Value
• e.g. such as a user ID
• proven technique for achieving data processing on a massive scale [1]
• solution used at the largest websites 
• e.g. Facebook, Twitter
• usually associated with rapid growth
•  ⇒ the approach needs to be dynamic [1]
• the only way to scale a relational database to massive web use [1]
• together with caching and replication [1]
• {drawback} involves significant operational complexities and compromises [1]
• the application must contain logic that understands the location of any particular piece of data and the logic to route requests to the correct shard [1]
• requests that can only be satisfied by accessing more than one shard thus need complex coding as well, whereas on a nonsharded database a single SQL statement might suffice.
• {drawback} high operational costs [1]
• {drawback} application complexity
• it’s up to the application code to route SQL requests to the correct shard [1]
• ⇒ a dynamic routing layer must be implemented
• ⇐ most massive websites are adding shards as they grow [1]
• layer required to maintain Memcached object copies and to differentiate between the master database and read-only replicas [1]
• {drawback} crippled SQL
• [sharded database] it is not possible to issue a SQL statement that operates across shards [1]
• ⇒ usually SQL statements are limited to row-level access [1]
• ⇒ only programmers can query the database as a whole [1]
• joins across shards cannot be implemented, nor can aggregate GROUP BY operations [1]
• {drawback} loss of transactional integrity
• ACID transactions against multiple shards are not possible and/or not practical [1]
• ⇐ {exception} there are database systems that support 2PC
• involves considerable troubleshooting as conflicts and bottlenecks can occur [1]
• {drawback} operational complexity. 
• load balancing across shards becomes extremely problematic
• adding new shards requires a complex rebalancing of data [1]
• changing the database schema requires a rolling operation across all the shards [1]
• ⇒ can lead to transitory inconsistencies in the schema [1]
• a sharded database entails a huge amount of operational effort and administrator skill [1]
• {concept} CAP (Consistency, Availability, and Partition) theorem 
• in a distributed database system, one can have at most only two of CAP tolerance [1]
• consistency
• every user of the database has an identical view of the data at any given instant [1]
• availability
• in the event of a failure, the database remains operational [1]
• partition tolerance
• the database can maintain operations in the event of the network’s failing between two segments of the distributed system [1]
• {concept} partitioning
• {def} core pattern of building scalable services by dividing state (data) and compute into smaller accessible units to improve scalability and performance [5]
• ⇐ determines that a particular service partition is responsible for a portion of the complete state of the service.
• a partition is a set of replicas)
• {type} [stateless services] a logical unit that contains one or more instances of a service [5]
• partitioning a stateless service is a very rare scenario
• scalability and availability are normally achieved by adding more instances
• {subtype} externally persisted state
• persists its state externally [5]
• e.g. databases in Azure SQL Database
• {subtype} computation-only services
• service that do not manage any persistent state e.g. calculator or image thumbnailing [5]
• {type} scalable stateful services
• partition state (data)
• a partition of a stateful service as a scale unit that is highly reliable through replicas that are distributed and balanced across the nodes in a cluster
• the state must be accessed and stored
• ⇒ bound by
• network bandwidth limits
• system memory limits
• disk storage limits
• {scenario} run into resource constraints in a running cluster
• {recommendation} scale out the cluster to accommodate the new requirements [4]
• {concept}distributed systems platform used to build hyper-scalable, reliable and easily managed applications for the cloud [6]
• ⇐ addresses the significant challenges in developing and managing cloud applications
• places the partitions on different nodes [5]
• allows partitions to grow to a node's resource limit
• ⇐ partitions are rebalances across nodes [5]
• {benefit} ensures the continued efficient use of hardware resources [5]
• {default} makes sure that there is about the same number of primary and secondary replicas on each node
• ⇒ nodes that hold replicas can serve more traffic and others that serve less traffic [5]
• hot and cold spots may appear in a cluster
• ⇐ it should be preferably avoided
• {recommendation} partition the state so is evenly distributed across all partitions [5]
• {recommendation} report load from each of the replicas for the service [5]
• provides the capability to report load consumed by services [5]
• e.g.  amount of memory, number of records
• detects which partitions server higher loads than others [5]
• ⇐ based on the metrics reported
• rebalances the cluster by moving replicas to more suitable nodes, so that overall no node is overloaded  [5]
• ⇐ it's not always possible to know how much data will be in a given partition
• {recommendation} adopt a partitioning strategy that spreads the data evenly across the partitions [5]
• {benefit} prevents situations described in the voting example [5]
• {recommendation} report load
• {benefit} helps smooth out temporary differences in access or load over time [5]
• {recommendation} choose an optimal number of partitions to begin with
• ⇐ there's nothing that prevents from starting out with a higher number of partitions than anticipated [5]
• ⇐ assuming the maximum number of partitions is a valid approach [5]
• ⇒ one may end up needing more partitions than initially considered [5]
• ⇐ {constraint} the partition count can't be changed after the fact [5]
•  ⇒ apply more advanced partition approaches
• e.g. creating a new service instance of the same service type
• e.g. implement client-side logic that routes the requests to the correct service instance

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References:

[1] Guy Harrison (2015) Next Generation Databases: NoSQL, NewSQL, and Big Data

[2] DAMA International (2017) "The DAMA Guide to the Data Management Body of Knowledge" 2nd Ed

[3] Microsoft Fabric (2024) External data sharing in Microsoft Fabric [link]
[4] Microsoft Fabric (2024) Data sharding policy [link]
[5] Microsoft Fabric (2024) Partition Service Fabric reliable services [link]
[6] MSDN (2015) Microsoft Azure - Azure Service Fabric and the Microservices Architecture [link]

Acronyms:

ACID - atomicity, consistency, isolation, durability

2PC - Two Phase Commit

CAP - Consistency, Availability, Partition