Limit bot activity to periods with less than 10k registered users online. Sparkbox Investments. Sparkbox is New Zealand’s leading early stage investor investing into high growth technology companies in the Asia Pacific region. Sparkbox has 134 repositories available. Follow their code on GitHub. Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0. Note that support for Scala 2.10 is deprecated as of Spark 2.1.0, and may be removed in Spark 2.3.0. Studio 2 Dayton, OH 45402 937.401.0915. One PPG Place Floor 31 Pittsburgh, PA 15222 Careers. Do you love the web as much as we do? Come work at Sparkbox. Interested in having Sparkbox train your team, speak at an event, or just want to introduce yourself?
This year, respondents shared challenges that span throughout the life of a design system. Three challenges stood out:
- Planning a design system that is built to last
- Managing changes to the design system
- Encouraging organizational adoption
Planning a Design System That Is Built to Last
In-house and agency respondents frequently mentioned needing a better plan and a more well-thought-out strategy.
In-house respondents were asked this open-ended question: if you had the ability to go back in time, what would you do differently with your design system? The most common in-house response mentioned having a better plan and a more well-thought-out strategy (18 out of 42 responses).
When agency respondents were asked why a client’s design system failed, they commonly reported the lack of a well-thought-out strategy (5 of 16 respondents).
Is your design system creating technical debt?
In addition, 42% of in-house respondents felt that the way their design system was originally built created debt for the organization’s technical or design departments.
When we asked how building the design system created debt, the two top responses were both related to a planning failure:
- Lack of planning (9 of 23 respondents)
- Failure to understand the magnitude of a design system (8 of 23 respondents)
Takeaway: Planning supports success
If hindsight is truly 20/20, planning and developing a clear strategy early in a design system’s life could contribute to its long-term success.
Are you planning a design system?
Learn how a Discovery phase can help you identify key details to make your design system a success. Or learn how to do an audit to decomp your website and identify and organize elements for future use.
![Sparkbox 1 24 Sparkbox 1 24](https://4.bp.blogspot.com/-f2Pj5UAj4ZQ/VuIXY1K1bxI/AAAAAAAAJbo/iqaVP7qmrf4eQxX20CJRWRbtEuYVWWu_g/s1600/valentine-mod-1.jpg)
Managing Changes to the Design System
What teams have maintenance processes?
In general, 63% of in-house respondents have a process for maintaining outdated, unused, or faulty components in their design systems. The age of the system impacted the likelihood to report having a maintenance process:
- Only 44% of respondents with a design system less than one year old have a maintenance process.
- For respondents with design systems that have existed for one year or longer, the percentage with a maintenance process grows to 77%.
The likelihood of having a process was also higher for those in-house respondents who called their design systems successful.
![Sparkbox 1 2 Sparkbox 1 2](https://miro.medium.com/max/11520/1*Souwk2hGxwHoiWLsVGz_8w.jpeg)
Does your organization have a process for maintaining design system components?
Yes, we have a process | No, we don't have a process | I don't know | |
---|---|---|---|
Not Very Successful Design System | 33% | 67% | 0% |
Not Successful Design System | 50% | 50% | 0% |
Neutral Design System | 56% | 38% | 6% |
Successful Design System | 78% | 11% | 11% |
Very Successful Design System | 100% | 0% | 0% |
No, we don't have a process
25%
75%
33%67%
50%50%
56%38%6%
78%11%11%
100%
Of the in-house respondents who reported their design systems to be unsuccessful, only 46% had a maintenance process.
Of the respondents who reported their design systems to be successful, 81% had a maintenance process.
Of the respondents who reported their design systems to be successful or very successful, 81% had a maintenance process. And that same group of respondents also viewed their change approval process as successful.
Of the in-house respondents who reported their design systems to be not successful or not very successful, only 46% had a maintenance process. Within this percentage, 55% of respondents perceived their maintenance process as unsuccessful.
Perhaps recognizing the complexity and importance in maintaining a design system, 89% of in-house respondents answered they rely on a single team and/or multiple teams to influence and approve changes to the design system.
Who approves changes to the design system?
A single team is responsible for approving changes. | 48% |
---|---|
A combination of a single team that manages the design system and a committee that influences decisions. | 25% |
A cross-team committee is responsible for approving changes. | 16% |
One individual oversees the design system. | 6% |
We don’t have a standardized process for this. | 3% |
Other | 1% |
10%
30%
50%
Answered79
Takeaway: Launching is just the beginning
Planning ahead for design system maintenance and approval responsibilities evidently contributes to the success of a design system.
Does your team struggle with maintaining your design system?
Discover the benefits of automated testing in the maintenance process and learn how your design system team is part of the product.
Encouraging Organizational Adoption
Since our first survey, respondents have reported adoption as a challenge, and that theme has continued this year.
In-house and agency respondents listed adoption as the top challenge faced in building, using, or maintaining design systems.
In-house respondents were asked an open-ended question about the biggest challenges they have faced in building, using, or maintaining their system. Adoption was the top answer, with 21 of 56 respondents mentioning it. When agency respondents were asked a similar question, the top reason was also adoption, with 8 of the 25 respondents mentioning it.
Respondents also highlighted the impact adoption has on a design system’s success.
If you feel that your organization’s design system was not successful, what were the primary reasons?
Staffing difficulties | 53% |
---|---|
Adoption difficulties | 52% |
Maintenance difficulties | 38% |
Lack of an executive champion | 36% |
Funding difficulties | 30% |
Company or departmental changes | 22% |
Other | 25% |
Sparkbox 1 2 0
20%
60%
Answered73
When in-house respondents were asked from a list of six options for the primary reason their design system is not successful, 52% of respondents said adoption difficulties, placing it second (just behind staffing difficulties).
Check this out!
Just 41% of in-house respondents said that they were actually tracking the adoption of their design system.
Takeaway: Adoption is a priority
Perhaps recognizing the importance of adoption, just under half of the in-house respondents said that improving design system adoption is a top priority for this upcoming year.
Is your team working on adoption?
Learn how to use scorecards to provide transparency and guidance for subscribers or learn how to overcome disengagement and maintain subscriber loyalty.
Apache Spark is a fast and general-purpose cluster computing system.It provides high-level APIs in Java, Scala, Python and R,and an optimized engine that supports general execution graphs.It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Get Spark from the downloads page of the project website. This documentation is for Spark version 2.2.1. Spark uses Hadoop’s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.Users can also download a “Hadoop free” binary and run Spark with any Hadoop versionby augmenting Spark’s classpath.Scala and Java users can include Spark in their projects using its Maven coordinates and in the future Python users can also install Spark from PyPI.
If you’d like to build Spark from source, visit Building Spark.
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It’s easy to runlocally on one machine — all you need is to have
java
installed on your system PATH
,or the JAVA_HOME
environment variable pointing to a Java installation.Spark runs on Java 8+, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.2.1uses Scala 2.11. You will need to use a compatible Scala version(2.11.x).
Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
Note that support for Scala 2.10 is deprecated as of Spark 2.1.0, and may be removed in Spark 2.3.0.
Spark comes with several sample programs. Scala, Java, Python and R examples are in the
examples/src/main
directory. To run one of the Java or Scala sample programs, usebin/run-example <class> [params]
in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit
script forlaunching applications). For example,You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.
The
--master
option specifies themaster URL for a distributed cluster, or local
to runlocally with one thread, or local[N]
to run locally with N threads. You should start by usinglocal
for testing. For a full list of options, run Spark shell with the --help
option.Spark also provides a Python API. Tuneskit spotify converter 1 7 0 2610. To run Spark interactively in a Python interpreter, use
bin/pyspark
:Example applications are also provided in Python. For example,
Spark also provides an experimental R API since 1.4 (only DataFrames APIs included).To run Spark interactively in a R interpreter, use
bin/sparkR
:Example applications are also provided in R. For example,
The Spark cluster mode overview explains the key concepts in running on a cluster.Spark can run both by itself, or over several existing cluster managers. It currently provides severaloptions for deployment:
- Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
Programming Guides:
- Quick Start: a quick introduction to the Spark API; start here!
- RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
- Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
- Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
- Spark Streaming: processing data streams using DStreams (old API)
- MLlib: applying machine learning algorithms
- GraphX: processing graphs
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API Docs:
Deployment Guides:
- Cluster Overview: overview of concepts and components when running on a cluster
- Submitting Applications: packaging and deploying applications
- Deployment modes:
- Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Mesos: deploy a private cluster using Apache Mesos
- YARN: deploy Spark on top of Hadoop NextGen (YARN)
- Kubernetes (experimental): deploy Spark on top of Kubernetes
Other Documents:
- Configuration: customize Spark via its configuration system
- Monitoring: track the behavior of your applications
- Tuning Guide: best practices to optimize performance and memory use
- Job Scheduling: scheduling resources across and within Spark applications
- Security: Spark security support
- Hardware Provisioning: recommendations for cluster hardware
- Integration with other storage systems:
- Building Spark: build Spark using the Maven system
- Third Party Projects: related third party Spark projects
External Resources:
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- Spark Community resources, including local meetups
- Mailing Lists: ask questions about Spark here
- AMP Camps: a series of training camps at UC Berkeley that featured talks andexercises about Spark, Spark Streaming, Mesos, and more. Videos,slides and exercises areavailable online for free.
- Code Examples: more are also available in the
examples
subfolder of Spark (Scala, Java, Python, R)