Browsing articles from "April, 2018"

Figuring out Consistency Levels in Azure Cosmos DB

Apr 18, 2018   //   by Karen Lopez   //   Azure, Blog, Cosmos DB, Data, Database, Database Design  //  No Comments

Azure Cosmos DB five levels of consistency Stront, Bounded Staleness, Session, Consistent Prefix and Eventual

I’ll have to admit: the first time I heard the term and explanation behind “Eventual Consistency”, I laughed.  This is due to the fact that I’ve spent my whole life fighting the good fight to ensure data is consistent.  That’s what transactions are for.  Now fast forward several years and we data professionals understand that some data stories don’t require strict consistency for ever reader of the data.

The key to that statement is reader. For the most part, we still don’t want inconsistent writes.

Consistency in a real world is a continuum from strictly consistent to eventually consistent.  Notice that consistency is still a goal.  But because it’s a continuum, there are many consistency schemes along the way.  I’ve always  struggled a bit with understanding and explaining these levels. 

We need these consistency levels due to the CAP Theorem, which says we can pick two of Consistency, Availability or Partition Tolerance when using distributed systems.  This is mostly due to physics: if I have distributed the same data over multiple locations, I need to give up one of the CAP items to make the system work. 

Let’s take a look at what the Cosmos DB documentation says about consistency levels (feel free to just scan this):

Consistency levels

You can configure a default consistency level on your database account that applies to all collections (and databases) under your Cosmos DB account. By default, all reads and queries issued against the user-defined resources use the default consistency level specified on the database account. You can relax the consistency level of a specific read/query request using in each of the supported APIs. There are five types of consistency levels supported by the Azure Cosmos DB replication protocol that provide a clear trade-off between specific consistency guarantees and performance, as described in this section.

Strong:

  • Strong consistency offers a linearizability guarantee with the reads guaranteed to return the most recent version of an item.
  • Strong consistency guarantees that a write is only visible after it is committed durably by the majority quorum of replicas. A write is either synchronously committed durably by both the primary and the quorum of secondaries, or it is aborted. A read is always acknowledged by the majority read quorum, a client can never see an uncommitted or partial write and is always guaranteed to read the latest acknowledged write.
  • Azure Cosmos DB accounts that are configured to use strong consistency cannot associate more than one Azure region with their Azure Cosmos DB account.
  • The cost of a read operation (in terms of request units consumed) with strong consistency is higher than session and eventual, but the same as bounded staleness.

Bounded staleness:

  • Bounded staleness consistency guarantees that the reads may lag behind writes by at most K versions or prefixes of an item or t time-interval.
  • Therefore, when choosing bounded staleness, the "staleness" can be configured in two ways: number of versions K of the item by which the reads lag behind the writes, and the time interval t
  • Bounded staleness offers total global order except within the "staleness window." The monotonic read guarantees exist within a region both inside and outside the "staleness window."
  • Bounded staleness provides a stronger consistency guarantee than session, consistent-prefix, or eventual consistency. For globally distributed applications, we recommend you use bounded staleness for scenarios where you would like to have strong consistency but also want 99.99% availability and low latency.
  • Azure Cosmos DB accounts that are configured with bounded staleness consistency can associate any number of Azure regions with their Azure Cosmos DB account.
  • The cost of a read operation (in terms of RUs consumed) with bounded staleness is higher than session and eventual consistency, but the same as strong consistency.

Session:

  • Unlike the global consistency models offered by strong and bounded staleness consistency levels, session consistency is scoped to a client session.
  • Session consistency is ideal for all scenarios where a device or user session is involved since it guarantees monotonic reads, monotonic writes, and read your own writes (RYW) guarantees.
  • Session consistency provides predictable consistency for a session, and maximum read throughput while offering the lowest latency writes and reads.
  • Azure Cosmos DB accounts that are configured with session consistency can associate any number of Azure regions with their Azure Cosmos DB account.
  • The cost of a read operation (in terms of RUs consumed) with session consistency level is less than strong and bounded staleness, but more than eventual consistency.

Consistent Prefix:

  • Consistent prefix guarantees that in absence of any further writes, the replicas within the group eventually converge.
  • Consistent prefix guarantees that reads never see out of order writes. If writes were performed in the order A, B, C, then a client sees either A, A,B, or A,B,C, but never out of order like A,C or B,A,C.
  • Azure Cosmos DB accounts that are configured with consistent prefix consistency can associate any number of Azure regions with their Azure Cosmos DB account.

Eventual:

  • Eventual consistency guarantees that in absence of any further writes, the replicas within the group eventually converge.
  • Eventual consistency is the weakest form of consistency where a client may get the values that are older than the ones it had seen before.
  • Eventual consistency provides the weakest read consistency but offers the lowest latency for both reads and writes.
  • Azure Cosmos DB accounts that are configured with eventual consistency can associate any number of Azure regions with their Azure Cosmos DB account.
  • The cost of a read operation (in terms of RUs consumed) with the eventual consistency level is the lowest of all the Azure Cosmos DB consistency levels.

    https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

It’s clear, isn’t it? No?  I’ll agree that reading text about consistency levels can be difficult to really understand.  in searching for more examples, I found a wonderful write-up that uses animations plus a baseball analogy. In that post,  Michael Whittaker  references the 2013 CACM article Replicated Data Consistency Explained Through Baseball (ACM Subscription required) by Doug Terry, of Microsoft Research.  If you don’t have access to the ACM library (you definitely should, by the way), you can find videos of talks he has given on this topic on the web.

Michael also has a more complex post on Visualizing Linearizability.  This is a topic I want to know more about, but first I have to tackle my challenge of saying Linearizability without stumbling.

It’s Always a Data Modeling Question…

Apr 9, 2018   //   by Karen Lopez   //   Blog, Data Modeling, Database Design  //  No Comments

WhatDoYouMeanByDataModel Question on a Beer Menu

When you have been a data modeler for [redacted] decades, you learn to see the world through data modeler eyes.  Everything seems to be a data modeling question.

I was with a client for lunch one day and we asked the server “What do you have on tap?”  She was gone quite a long time, but came back and said “Beer.”  It turned out she was right.  But her answer was not that helpful.

Why is an Expert Asking Us What a Data Model Is?

One of the odd parts of every new project I have to deal with is getting everyone to understand that the question “Can you help us with a data model” results in me asking. “What do you mean by data model?” That’s right, I have to ask team members what a data model is. You’d think experts would know better.

I have to do this because it seems like everyone has a different definition.  For most DBAs, they want a reverse-engineered image of a production database.  For a business user, data modeling that results in documentation about all the questions, answers and decisions were made. For a developer a specification of something they can build upon. An executive wants a high-level view of the data concepts a specific project will be addressing so she can approve scope and budgets.  A data scientist wants a consolidated view of both the physical data objects available to him and a logical definition of what they are. Finally, a data modeler wants a list of previously modeled entities so that she doesn’t have start from scratch on every project.

It’s likely that every role in the organization wants a different data model with a different set of metadata. It’s also why we need to have a discussion about conceptual, logical and physical data models.   Even that set of terms has differing definitions. That’s nearly unforgivable given that we data modelers preach that we should use consistent definitions. (Note from author: this one bit led to one of the longer threads on LinkedIn that has ever been discussed about one of my posts. Some of the comments are not fit for work.) Then to make this even more complex, we need to discuss the primitives in the Zachman Framework as well.

Simple Tools Don’t Work Well For Complex Data Model Questions

This is why using native database tools aren’t good enough to solve all those needs.  This is why a simple drawing tool isn’t enough.  What an enterprise needs are tools that can author, design, and present all those types of data models without creating duplicate copies of those data concepts. It’s also why a data modeler needs to ask the question: What do *you* mean by data model?  You need to ask your team members what they are expecting before you start working.  You may need to negotiate priorities or formats.  You may need to create separate views of your models. That’s wonderful, though, to deliver what they need from your data models. It’s all good.

It’s not that we modelers don’t know the 100+ possible answers to that question.  It’s that we know there are 100+ answers.

It’s not that we modelers don’t know the 100+ possible answers to that question.  It’s that we know there are 100+ answers. That’s what data modeling is after all: getting to the right answer for this requirement.

Note: This post is an updated version of one posted to community.embarcadero.com in 2015

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