M Speed Test

NDT is a single stream performance measurement of a connection’s capacity for “bulk transport” (as defined in IETF’s RFC 3148. NDT measures “single stream performance” or “bulk transport capacity”. NDT reports upload and download speeds and latency metrics.

Run an NDT Test

If you are interested in running an NDT test, please visit our site: https://speed.measurementlab.net

Measurement Lab (M-Lab) Speed Test Information DPI is committed to improving digital equity in the state of Wisconsin. As part of our efforts, DPI is collaborating with Measurement Lab (M-Lab) to collect data on internet connection speeds across Wisconsin. DPI will use M-Lab’s internet speed test data to create detailed reports and to provide maps of internet connection speeds across the. All trademarks of Ookla, LLC, including Speedtest®, are used under license. Measurement Lab (M-Lab) Speed Test Information DPI is committed to improving digital equity in the state of Wisconsin. As part of our efforts, DPI is collaborating with Measurement Lab (M-Lab) to collect data on internet connection speeds across Wisconsin. DPI will use M-Lab’s internet speed test data to create detailed reports and to provide maps of internet connection speeds across the.

History

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Originally developed at Internet2, M-Lab has hosted NDT since our founding in 2009, and helped maintain and develop NDT for most of its history on the M-Lab platform. Over the last decade, there are three primary themes that have driven the evolution of NDT: standard kernel instrumentation, advances in TCP congestion control, and protocols and ports to support more clients. For more information, please see our blog post discussing the Evolution of NDT.

NDT Testing Protocols

As a part of our transition from the web100 version of NDT server to the new platform, M-Lab named specific protocol versions for the original server and the new one we are now using. We now refer to these as “datatypes” for the NDT test.

  • web100 is the protocol referring to data collected by the original NDT server
    • Relied on the web100 kernel module for TCP statistics
    • Collected using the original version of NDT server
    • Used the Reno TCP congestion control algorithm
    • Ran from 2009-02-18 to 2019-11-20
  • ndt5 is an NDT protocol designed to be backward compatible with legacy NDT clients
    • Relies on tcp-info for TCP statistics
    • Collected using ndt-server, which follows the legacy NDT protocol to support existing NDT clients that use it
    • Uses the Cubic TCP congestion control algorithm
    • Started 2019-07-18 and continues to present. The evolution to ndt7 is driven by client upgrades and is expected to have a very long tail
  • ndt7 is an NDT protocol that uses TCP BBR where available, operates on standard HTTP(S) ports (80, 443)
    • Relies on tcp-info for TCP statistics
    • Collected using ndt-server
    • Uses the BBR TCP congestion control algorithm, falling back to Cubic when BBR is not available on the client side
    • Started 2020-02-18 and continues to present

Data Collected by NDT

When you run NDT, the IP address provided by your Internet Service Provider will be collected along with your measurement results. M-Lab conducts the test and publishes all test results to promote Internet research. NDT does not collect any information about you as an Internet user.

Please review M-Lab’s Privacy Policy to understand what data is collected and how data is used before initiating a test.

Unparsed Raw NDT Data in GCS

All of the raw data and log files from the measurement fleet are archived in their original format and available in Google Cloud Storage. As our parsing and analysis algorithms improve, M-Lab periodically reprocesses all of this archived data.

Generally, BigQuery rows indicate the locations of the raw data from which they were derived. Dedicated users can reconstruct our analysis and in principle fully replicate our parsers. The raw data also includes TCP packet captures (.pcap files) for most NDT tests, however the pcap files are not indexed in BigQuery yet. Details on how M-Lab publishes test data in raw form are provided on our Google Cloud Storage documentation page.

NDT Data in BigQuery

To make NDT data more readily available for research and analysis, M-Lab parses all NDT data into BigQuery tables and views, and makes query access available for free by subscription to a Google Group. Find out more about how to get access on our BigQuery QuickStart page.

Note that we sometimes use the terms “table” and “view” interchangeably: they reflect different internal implementations, but due to billing and access controls everything documented here as a table is actually presented as a view.

The presentation of NDT data in a series of datasets and views in BigQuery represents M-Lab’s strategy for data curation, providing a cleaned and filtered view of test results that can be used to attempt answering the most common research questions of our community requiring known good test results. By also preserving raw test data as collected and annotated, and curating views in intermediate steps, we can also support users whose research is concerned with unfiltered or non-curated tests.

We now publish three series of Datasets in BigQuery containing Views for NDT data. These datasets and views mirror the processing stages of our ETL pipeline:

DatasetDescription
measurement-lab.ndt.*Unified Views in the ndt dataset present a stable, long term supported unified schema for all ndt datatypes (web100, ndt5, ndt7), and filter to only provide tests meeting our team’s current understanding of completeness & research quality as well as removing rows resulting from M-Lab’s operations and monitoring systems.
measurement-lab.ndt_intermediate.*Extended Views in the ndt_intermediate dataset join raw measurements with annotations, and remap column names across all ndt datatypes (web100, ndt5, ndt7) to provide a common schema for use in the Unified Views. M-Lab does not guarantee long term supported schemas for Views in the ndt_intermediate dataset. Researchers using these views should be aware that breaking schema changes in future releases may affect your queries.
measurement-lab.ndt_raw.*Raw Views in the ndt_raw dataset provide a 1-to-1 mapping of tests contained in GCS archives to test rows.

Unified Views

NDT Unified Views are published in the ndt dataset, and are designed to easily support studies of the evolution of the Internet performance by geopolitical regions.

Unified Views should be the starting point for most people.

NDT Unified Views:

  • Use a standardized schema across all ndt datatypes (ndt7, ndt5 and web100)
  • Present computed performance metrics (i.e. data rate, loss rate, min RTT and more in the future)
  • Have separate views for upload and download because the test details and data processing are different for each direction
  • Are strict subsets (rows and columns removed) of the union of the Extended Views
  • Are curated to only include tests that meet our current, best understanding of completeness and research quality:
    • At least 8 KB of data was transferred (extends below 9.6 kbits/second)
    • Test duration was between 9 and 60 seconds
    • For downloads, some form of network congestion was detected (i.e. tests with only non-network bottleneck are excluded)
    • Tests with parser errors and NULL results are excluded
    • Tests from M-Lab Operations and Management (OAM) infrastructure are excluded
  • Also called “Helpful Views” in past documentation and blog posts

In BigQuery, unified views are prepended with unified_:

Unified views with suffixes resembling dates (i.e. unified_uploads_20201026x) are provided to support differential A/B testing across processing changes. They give researchers a easy way to detect if our changes have any affect on downstream research results.

For more background on unified views see the blog posts below, noting that some of the terminology has evolved slightly since the blog posts.

Extended Views

NDT Extended Views are published in the ndt_intermediate dataset, and contain every row from the raw views, with added columns describing everything that we know about the data.

Custom unified views based on the NDT Extended Views should be the starting point for nearly all alternative analyses of M-Lab data.

For guidance and examples please see: Creating Custom Unified Views or Subqueries for Your Own Research

NDT Extended Views:

  • Have no filters applied but every row is labeled with the selection criteria used by the unified views
  • Contain calculated metrics and other standard columns such as: data rate, loss rate, minimum RTT, etc.
  • Are joined with geographical annotations
  • In the Future will be joined with traceroute and other data sets such as platform load telemetry and Internet health indicators
  • Have schemas are supersets of the unified view schema and raw tables schemas, differing per experiment and raw parser version
  • Are designed to support user-implemented Custom Unified Views
  • In BigQuery, extended views are in the dataset measurement-lab.ndt_intermediate:
    • measurement-lab.ndt_intermediate.extended_ndt7_downloads
    • measurement-lab.ndt_intermediate.extended_ndt7_uploads
    • measurement-lab.ndt_intermediate.extended_ndt5_downloads
    • measurement-lab.ndt_intermediate.extended_ndt5_uploads
    • measurement-lab.ndt_intermediate.extended_web100_downloads
    • measurement-lab.ndt_intermediate.extended_web100_uploads

Raw Views

NDT Raw Views are published in the ndt_raw dataset, and provide a 1-to-1 mapping of tests contained in our Google Cloud Storage archives to test rows, and are the closest representation of archived raw test data that has been parsed and imported into BigQuery.

NDT Raw Views are provided for completeness and transparency but are no longer recommended for general use.

NDT Raw Views:

  • Include one row for every unique test that can be parsed, even if truncated or partially corrupted
  • Contain a small number of added columns indicating parse errors and (future) metrics computed directly from the snap logs (web100 or tcp-info)
  • The schemas reflect the original structure of the archived raw data and differ per tool and parser version
  • They are subject to breaking changes
  • Also called “faithful views” in past documentation and blog posts
  • With names ending in _legacy were generated by an older parser version and are slated to be replaced in the future
  • In BigQuery, raw views are in the dataset ‘measurement-lab.ndt_raw`:
    • measurement-lab.ndt_raw.ndt7
    • measurement-lab.ndt_raw.ndt5_legacy
    • measurement-lab.ndt_raw.web100_legacy
    • measurement-lab.ndt_raw.tcpinfo_legacy
    • measurement-lab.ndt_raw.traceroute_legacy
    • measurement-lab.ndt_raw.annotation

Example Queries and Updating Past Queries

If you need examples or assistance updating past research queries to use our current BigQuery Views, please review the pages below:

Source Code

NDT Server

NDT Reference Clients

Citing the M-Lab NDT Dataset

Please cite the NDT data set as follows: The M-Lab NDT Data Set, <date range used> https://measurementlab.net/tests/ndt

or, in BibTeX format:


Policies & Support Information

NDT is maintained by Measurement Lab, and is governed by the M-Lab Privacy Policy.

For support with NDT please email [email protected]

Changelog for NDT Schemas in BigQuery

Generally, schemas for all M-Lab datasets are published as tagged releases in the etl-schema repository on Github. This section outlines changes specific to NDT schemas over time.

[v3.17] - https://github.com/m-lab/etl-schema/releases/tag/v3.17

  • Renames publicly available datasets to mirror naming in our ETL process, and aligns alphabetical names of NDT datasets in BigQuery for better readability.
    • ndt_intermediate renamed. Previously named intermediate_ndt.
    • ndt_raw renamed. Previously named raw_ndt.
  • Renames views in the raw_ndt dataset, adding the suffix, _legacy to raw views of data collected using now deprecated or legacy parsers and/or kernel instrumentation.
  • Minor bug fixes to “Unified Views” in the ndt dataset.
    • BQ_SAFE operators were added to queries that generate NDT Unified Views to force any rows with corrupted geographic annotated fields to be expressed as NULLs.
    • Congestion Control Algorithm (CCA) for upload tests were reporting the server’s Congestion Control Algorithm as the client’s CCA. This was partially a legacy bug of the old web100 based tooling, that reported upload and download results in the same row. Upload tests will no longer contain the client CCA since it is not currently passed to the server at test time.
  • Version numbers dropped from this changelog, and moving forward will transition to providing detailed release notes on Github releases, replacing this changelog.

v3.11.0 - 2020-04

  • Following the M-Lab 2.0 platform upgrade completed in November 2019
    • NDT data from the now deprecated web100 based ndt has been archived in the dataset measurement-lab.ndt.web100
    • NDT data from the new, TCP INFO based ndt-server is now provided in measurement-lab.ndt.ndt5
    • associated TCP INFO data for all ndt5 tests is now provided in measurement-lab.ndt.tcpinfo
  • Views from web100 ndt are now deprecated, superceded by new “unified” views
    • The following Views provide access only to data from the web100 legacy platform:
      • measurement-lab.ndt.recommended
      • measurement-lab.ndt.downloads
      • measurement-lab.ndt.uploads
  • Unified views of all NDT data published
    • Two new historical views of all NDT data are now available, and provide only NDT tests that meet our [criteria] for valid, research quality tests.
      • measurement-lab.ndt.unified_downloads
      • measurement-lab.ndt.unified_uploads

[v4] - 2019-05

  • In previous release convention a hierarchy of releases, release candidates “rc”, versioned release candidates, and versioned intermediate views were published, but they will cease being updated with new data starting May 6, 2019.
  • BigQuery datasets named after M-Lab measurement services & data types.
  • Each measurement service (ndt, traceroute, sidestream, utilization) will have a corresponding BigQuery dataset and view in the measurement-lab project, managed by our data reprocessing service.
  • LegacySQL support is now deprecated, but a single LegacySQL view of the legacy data may be kept for historical purposes.
  • Only StandardSQL is supported in any new views of the comprehensive reprocessed data.
  • Views that combine legacy tables and recently parsed data will no longer be offered.
  • Historically, Paris Traceroute data was collected for every measurement service. For this data type, a view in the aggregate dataset is now provided.
  • Over the next year, M-Lab will restructure the traceroute schema to support reprocessing using the Gardener service, and to unify the schema for historical and future data collection by Scamper.

[v3.1.1] - 2018-07

  • Publish official Switch tables from the DISCO dataset.

Published tables and views are:

  • measurement-lab.legacy.ndt (data ~ 2015-01-01 - 2017-05-10)
  • measurement-lab.legacy.ndt_pre2015 (data ~ 2009-02-18 - 2014-12-31)
  • measurement-lab.base_tables.ndt
  • measurement-lab.base_tables.switch

  • measurement-lab.rc
  • measurement-lab.release_v3_1
  • measurement-lab.release
    • measurement-lab.release.ndt_all
    • measurement-lab.release.ndt_all_legacysql
    • measurement-lab.release.ndt_downloads
    • measurement-lab.release.ndt_downloads_legacysql
    • measurement-lab.release.ndt_uploads
    • measurement-lab.release.ndt_uploads_legacysql

[v3.1] - 2018-02

Internet
  • First official release of v3 tables, with all historical data re-parsed, and annotated with geolocation metadata.

[v3.0.2] - 2017-12

  • Standardized the naming scheme for BigQuery table and view names to be consistent with new semantic versioning.
  • All tables and views must be queried using StandardSQL, except for views with “legacysql” in the name.
  • Views for tests other than NDT may be published in the future using the same format:
    • <test>_all_<version> (standardSQL)
    • <test>_all_legacysql_<version>
  • Complete documentation for tables, views, the contents of views, and what data they limit (where applicable) will be published on this page.
  • Views will be published concurrently with new table schemas, such that all table versions will have corresponding views.
  • Previous versions of our tables will be referenced by versions 1.0, 2.0, etc. in our documentation but actual table names will not be changed.
  • Re-ran historical annotations for traceroute, npad, and sidestream data due to a bug where some geolocation annotations was not present in all past test data.

[v3.0.1] - 2017-10

  • The schema for v3.0.1 tables was updated, removing an alpha feature called deltas, which attempted to log the differences between test snaplogs instead of the final test values. This feature will be revisited in future schema updates.
  • Newly released data annotation engine added geolocation and some metadata to tests from 2016 to present.
  • Published a series of beta BigQuery views for NDT data, to allow data queries across both v2 and v3.0.x tables.
  • Published traceroute and sidestream table to replace v2 versions, migrated data, re-annotated data.

[v3] - 2017-05

  • Began publication to new date partitioned table and updated schema to support the new, open source, ETL pipeline.
  • Data publication to v2 tables stopped at this time.

[v2.1] - 2016-11

  • The field blacklist_flags was added to v2 per project “fast tables”, and historical data from 201001-01 to 2015-10-02 was re-parsed to add this annotation, due to a switch discard issue related to traffic microbursts.

[v2] - 2016-03

  • Began the publication of per project “fast tables” for NDT, NPAD, Paris Traceroute, and Sidestream.
    • plx.google:m_lab.ndt.all
    • plx.google:m_lab.npad.all
    • plx.google:m_lab.paris-traceroute.all
  • Continued the publication of v1 monthly tables, and published a migration guide.
  • Deprecated fields in v2 “fast tables”:
    • type
    • project
    • web100_log_entry.is_last_entry
    • web100_log_entry.group_name

Measurement Lab (M-Lab) Speed Test Information

DPI is committed to improving digital equity in the state of Wisconsin. As part of our efforts, DPI is collaborating with Measurement Lab (M-Lab) to collect data on internet connection speeds across Wisconsin. DPI will use M-Lab’s internet speed test data to create detailed reports and to provide maps of internet connection speeds across the state. These reports can help the Governor’s Task Force on Broadband Access and other broadband task groups target initiatives to improve internet speeds statewide.

About the Data

What dashboards are available to me today to view speed test data from M-Lab? M-Lab provides premade dashboards to report on internet speeds, collected via their standard NDT (Network Diagnostic Tool), which are averaged over larger areas like school districts and counties. Data also includes provider speeds within those areas.

What data can I view from the M-Lab speed test? M-Lab provides a dashboard that can answer the following question: What are the average speeds from a zip code, city, county, congressional district or state perspective?

Click Speed Test

What data can I view specifically from a school district perspective? M-Lab created a dashboard specifically at DPI’s request that can answer the following question: What are the average speeds within school districts, broken down by ISP? For detailed information about this report, refer to the District Broadband Speed Report Information page.

Has the FCC provided household broadband guidelines? Yes. Their household broadband guidelines are here.

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Tests Performed So Far

Below is a graph showing the cumulative number of speed tests since the beginning of this speed testing campaign.

Participate in the Collection

To help collect data on internet speeds in your community, add a link to the M-Lab Speed Test on your school, district, or library's website, with an invitation for visitors to test their internet speed. We encourage people to run the speed test as many times as they want: the more data is available on internet speeds, the better we can target our efforts to improve internet connectivity in Wisconsin.

The URL for the M-Lab Speed Test is https://speed.measurementlab.net/#/.

You can also get the word out through email and social media. To get started, you can use the email templates and images in the digital equity communication toolkit.

Take the Speed Test in Three Simple Steps

  1. On a device connected to your home internet, click on the link to the M-Lab broadband speed test.

  2. Agree to the Data Privacy Policy.

  3. Click Begin to start the test.

    • Consider running the test multiple times, at different times of day, as your internet speed can often vary throughout the day.

Frequently Asked Questions

1. What is Measurement Lab? Measurement Lab, or M-Lab, is an open source project dedicated to measuring internet performance around the world creating a public data set of internet performance. This data is used by researchers, regulators, advocacy groups, and the general public to advocate for improved internet performance and maintain network quality. You can learn more about Measurement Lab on their About page.

2. What is M-Lab's data privacy policy? M-Lab publishes a detailed policy governing how internet performance data is used and what information is collected. Refer to the M-Lab Privacy Policy page for detailed information.

3. What if my district uses a proxy server to filter student access? The M-Lab speed test measures the internet speed of the proxy server, not the originating device. This means that if your school district redirects internet traffic through a proxy server for school-issued computers, the internet speed of those devices will not be measured. If your school district uses a proxy server, we recommend that you either do not put the speed test link on your district web site, or that you include language saying that speed tests should not be taken from school-issued computers. Note that third party filtering products such as Securely and GoGuardian do not use proxy servers to filter student access.

4. Are speed tests reliable? In general, yes. There are factors that can impact measured speed which are listed below. The importance of having many different tests taken by many different internet users at various days, times, and conditions is what leads to a more complete and realistic picture of internet speeds.

Charter Speed Test

  • Subscriber speed selection. If a consumer has an internet speed option of 1Gbps, but they only choose to purchase 25Mbps/3Mbps service the test reflect that consumer’s choice, not the available speed.
  • Additional Wi-Fi enabled devices. If there are other Wi-Fi devices or users connected to a network at the same time (i.e. multiple telecommuters on videoconferences, students distance learning or streaming HD video), impact the connectivity of the device performing the speed test.
  • Network bandwidth consumption on the testing device. Running an application like Netflix while running a speed test affects results.
  • Age and placement of equipment. Building materials in walls or other objects in the home or business interfere with a Wi-Fi signal. A router near appliances or metal objects that emit electromagnetic waves disrupt a Wi-Fi signal. An older computer may have an older network card incapable of the measuring internet speeds offered by a provider. An older router is likely to perform significantly worse compared to ones that are updated. Wi-Fi extenders and boosters are popular fixes when a router has a small antennae, which can impact the accuracy of a speed test.
  • Using a virtual private network (VPN). Using a VPN or cellpadding='1' cellspacing='1'>ContactInformation

    Contact: Glenn Fishbine

    Features offered beyond the standard M-Lab test being promoted by DPI:

    • Provides maps of tests taken to date, including provider, location, speed, and cost/month
    • Shows results by week, month, quarter, or year
    • Allows users to manually enter their address, drop a pin onto a map to show their address, or autodiscover their address (less accurate)
    • Captures and maps unserved locations (entered by user from another location)

    Contact: Brian Rathbone

    828.223.4053

    Features offered beyond the standard M-Lab test:

    • Maps speed, provider, and demographics down to the the census tract level
    • Provides maps of tests taken to date, including provider, location, speed, and cost/month
    • Includes demographic data
    • Allows users to manually enter their address; autocompletes address to ensure correct geolocation; allows “fine tuning” of address by dropping pin on map
    • Captures and maps unserved locations (entered by user from another location)
    • Compares advertised vs. actual internet speed
    • Provides guidance to user on recording most accurate speed
    • Enables customer to customize survey questions at start of survey

    6. What are the benefits to contracting with a company to analyze internet speed within a specific area? As noted from the two provider examples, there are additional questions you can answer and data you can gather with a paid service that collects address level data for conducting speed tests such as:

    • What are the average speeds for specific addresses? This information makes it possible to identify small areas where service is poor.
    • Which specific addresses have no service?

    The speed test data from any other provider generally can be added to M-Lab’s dataset just as when you use M-Lab’s standard test. DPI encourages anyone using other services to explore integrating their data with M-Lab's to enhance the overall statewide database. We would be happy to discuss those integration options with you.