Apache Spark™ - Unified Engine for large-scale data analytics

COLLECTED BY

Organization: Archive Team

Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.

History is littered with hundreds of conflicts over the future of a community, group, location or business that were "resolved" when one of the parties stepped ahead and destroyed what was there. With the original point of contention destroyed, the debates would fall to the wayside. Archive Team believes that by duplicated condemned data, the conversation and debate can continue, as well as the richness and insight gained by keeping the materials. Our projects have ranged in size from a single volunteer downloading the data to a small-but-critical site, to over 100 volunteers stepping forward to acquire terabytes of user-created data to save for future generations.

The main site for Archive Team is at archiveteam.org and contains up to the date information on various projects, manifestos, plans and walkthroughs.

This collection contains the output of many Archive Team projects, both ongoing and completed. Thanks to the generous providing of disk space by the Internet Archive, multi-terabyte datasets can be made available, as well as in use by the Wayback Machine, providing a path back to lost websites and work.

Our collection has grown to the point of having sub-collections for the type of data we acquire. If you are seeking to browse the contents of these collections, the Wayback Machine is the best first stop. Otherwise, you are free to dig into the stacks to see what you may find.

The Archive Team Panic Downloads are full pulldowns of currently extant websites, meant to serve as emergency backups for needed sites that are in danger of closing, or which will be missed dearly if suddenly lost due to hard drive crashes or server failures.

Collection: ArchiveBot: The Archive Team Crowdsourced Crawler

ArchiveBot is an IRC bot designed to automate the archival of smaller websites (e.g. up to a few hundred thousand URLs). You give it a URL to start at, and it grabs all content under that URL, records it in a WARC, and then uploads that WARC to ArchiveTeam servers for eventual injection into the Internet Archive (or other archive sites).

To use ArchiveBot, drop by #archivebot on EFNet. To interact with ArchiveBot, you issue commands by typing it into the channel. Note you will need channel operator permissions in order to issue archiving jobs. The dashboard shows the sites being downloaded currently.

There is a dashboard running for the archivebot process at http://www.archivebot.com.

ArchiveBot's source code can be found at https://github.com/ArchiveTeam/ArchiveBot.

TIMESTAMPS

Run now

Install with 'pip'

$ pip install pyspark

$ pyspark

Use the official Docker image

$ docker run -it --rm spark:python3 /opt/spark/bin/pyspark

df = spark.read.json("logs.json")
df.where("age > 21").select("name.first").show()

# Every record contains a label and feature vector
df = spark.createDataFrame(data, ["label", "features"])

# Split the data into train/test datasets
train_df, test_df = df.randomSplit([.80, .20], seed=42)

# Set hyperparameters for the algorithm
rf = RandomForestRegressor(numTrees=100)

# Fit the model to the training data
model = rf.fit(train_df)

# Generate predictions on the test dataset.
model.transform(test_df).show()

df = spark.read.csv("accounts.csv", header=True)

# Select subset of features and filter for balance > 0
filtered_df = df.select("AccountBalance", "CountOfDependents").filter("AccountBalance > 0")

# Generate summary statistics
filtered_df.summary().show()

Run now

$ docker run -it --rm spark /opt/spark/bin/spark-sql

spark-sql>

SELECT
  name.first AS first_name,
  name.last AS last_name,
  age
FROM json.`logs.json`
  WHERE age > 21;

Run now

$ docker run -it --rm spark /opt/spark/bin/spark-shell

scala>

val df = spark.read.json("logs.json")
df.where("age > 21")
  .select("name.first").show()

Run now

$ docker run -it --rm spark /opt/spark/bin/spark-shell

scala>

Dataset df = spark.read().json("logs.json");
df.where("age > 21")
  .select("name.first").show();

Run now

$ docker run -it --rm spark:r /opt/spark/bin/sparkR

df <- read.json(path = "logs.json")
df <- filter(df, df$age > 21)
head(select(df, df$name.first))

Feb	MAR	Apr
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2025	2026	2027

Unified engine for large-scale data analytics

What is Apache Spark™?