Donnie Berkholz leads the development, DevOps and IT ops team at 451 Research. In this talk, he will draw on his experience and research to discuss emerging trends in how software across the stack is created and deployed, with a particular focus on relevance to storage development and usage. Donnie will discuss the potential impacts of these trends to how storage software is built as well as what kinds of new use cases it needs to support.
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Emerging trends in software development: The next generation of storage
1. Emerging trends
in software development:
The next generation of storage
Donnie Berkholz, Ph.D.
Research Director — Development, DevOps & IT Ops
SNIA Storage Developer Conference, Sept 2015
3. App-dev workloads are quickly moving to the cloud
3
Current Future
59.0%
7.9%
14.2%
5.2%
6.6%
7.1%
On-Premises, Non-Cloud
Off-Premises, Non-Cloud
On-premises Private Cloud
Hosted Private Cloud
Infrastructure-as-a-Service
(IaaS)/Public Cloud
Software-as-a-Service (SaaS)
n = 146
37.3%
6.1%
19.1%
9.9%
15.2%
12.4%
n = 134
Source: 451 VotE Cloud, Q2 2015
4. CLOUD COMPUTING
Q1 2015
Q. Estimate the percentage of your organization’s data currently stored in off premises cloud environments? Q. How many terabytes of data does your
company currently have under management across all environments?
*Terabytes of Data Stored in Off-Premises Cloud" is calculated from an organization's total data and the percent they store in off-premises cloud
environments.
Data Storage - Cloud Respondents
4
20.0%
7.5%
7.3%
6.3%
42.8%
16.2%
> 1000TB
750TB-1000TB
500TB-749TB
250TB-499TB
10TB-249TB
<10TB
Percent of Sample
n =877
Percent of Organization’s Data
Stored in Off-Premises Cloud
Total Data at Organization
Source: 451 Research, Voice of the Enterprise: Cloud Computing Q1 2015
3.8%
1.4%
2.5%
3.1%
42.0%
47.1%
> 1000TB
750TB-1000TB
500TB-749TB
250TB-499TB
10TB-249TB
<10TB
Percent of Sample
n =766
Terabytes of Data Stored in Off-Premises
Cloud *
7.8%
6.4%
6.9%
14.6%
64.3%
> 80%
60%-80%
40%-59%
20%-39%
< 20%
Percent of Sample
n =896
11. But it’s not just a toy
11
21%}
Source: 451 VotE Cloud, Q1 2015; n=991
3.1%
19.8%
56.1%
10.7%
3.9%
4.2%
2.1%
Unfamiliar
No Plans
Discovery and Evaluation
Running Trials/Pilot Projects
Used for Test and Development
Environment
Initial Implementation of Production
Applications
Broad Implementation of
Production Applications
13. Loosely coupled teams
“ One of the biggest changes is that we no longer have
an official ‘architecture’ team. Instead, we have made
‘architecture’ an ‘ingredient’ on each of our teams.”
13
http://tech.gilt.com/post/102628539834/making-architecture-work-in-microservice
– Lauri Apple, Gilt Groupe, 14 Nov 2014
36. Wrapping up
• Docker and microservices will exacerbate hypervisor-style use &
increase demands on the network
• Spark will drive requirements for memory and memory-like storage
• DevOps & continuous delivery will transform how you deliver
storage software
• The interplay between open source, standards and benchmarks
continues to tilt toward agility
36
38. Storage needs to cope with agility.
Donnie Berkholz
@dberkholz
donnie.berkholz@451research.com
39. Some images from this presentation
are Creative-Commons licensed.
https://creativecommons.org/licenses/by-sa/
https://creativecommons.org/licenses/by/
39
Hinweis der Redaktion
Cathedral, indulgences to bazaar
Open source, cloud, DigitalOcean
Future is two years out
App dev is 7.8% of workloads, ranked #5 (highest is email/collab at 15.9%)
… What does this mean for the data behind the apps?
Data gravity. Still mostly on-prem.
… that’s what we’re building, now how are we building it?
Languages, databases, frameworks
… What’s driving the way we build technology?
The new mode for access & transfer of data has become APIs
Most storage file/block. This is more likely object-driven
… why should you and your companies care?
N not large enough for everything
Exciting vendors: Pure and EMC
Hypervisors already had randomized I/O, now exacerbated. Flash optimal, tough for SMR
Flocker BoF
Explain GitHub, Stars/PRs/Forks
Active-active, cross-DC/cross-region (DynamoDB outage)
Distributed datastores
Most storage backup/replication is array-/hypervisor-level, not app-level
Loosely coupled
Business-defined separations.
Bounded context based on cross-organizational empathy.
Steve Yegge memo — Amazon must be SOA, or you’re fired.
DevOps + microservices
Bounded contexts, empathy defined
DevOps is how you build and run microservices.
Also note PaaS providers moving to containers
Spark. What is Spark? In-memory compute, can cache to disk.
IoT
In-memory compute
Real-time analytics (streaming), graph, ML (repeated computations in memory)
NoSQL at scale – distributed databases (Cassandra)
Spark. What is Spark? In-memory compute, can cache to disk.
IoT
In-memory compute
Real-time analytics (streaming)
NoSQL at scale – distributed databases (Cassandra)
Hadoop market
26 vendors included
42% CAGR above $100m rev
50% below $100m rev
… A couple of quick hits before we get into how you change
Do app devs need to care?
Storage round trips much cheaper, how does this change things?
Suitable data structures – parallels to Redis/Memcached?
IoT (equivalent of moving compute to data as SCM reaches CPU)
Their view: onload/offload and RDMA
Mine: functional programming, JVMs, Go, distributed systems, Spark, etc.
OODA loops
How can SP do this? Marketing, PS, dev-focused services, SDKs etc
Where storage and networking fit in. APIs, vanilla (SDS, Cumulus, etc)
Release management – quarters to weeks to days to hours
Gary Gruver, HP