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Last active Aug 18, 2022
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Meadowrun-wikipedia-demo
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Brown & Brown
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CarMax
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CDW
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Comcast
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CooperCompanies
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D.R. Horton
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Delta Air Lines
Dentsply Sirona
Devon
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Discover
Dish
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Dollar Tree
Dominion Energy
Domino's
Dover
Dow
DTE
Duke Energy
Duke Realty
DuPont
DXC Technology
Eastman
Eaton
eBay
Ecolab
Edison International
Edwards Lifesciences
Electronic Arts
Emerson
Enphase
Entergy
EOG Resources
EPAM
Equifax
Equinix
Equity Residential
Essex
Estée Lauder Companies
Etsy
Everest
Evergy
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Expedia Group
Expeditors
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F5
FactSet
Fastenal
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FedEx
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First Republic
FirstEnergy
FIS
Fiserv
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FMC
Ford
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Gallagher
Garmin
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Generac
General Dynamics
General Mills
Genuine Parts
Gilead
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GM
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Grainger
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Healthpeak
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Hilton
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Honeywell
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Howmet Aerospace
HP
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Intel
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IPG Photonics
IQVIA
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Jacobs
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Linde
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Lowe's
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Masco
Mastercard
Match Group
McCormick
McDonald's
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Medtronic
Merck
Meta
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MGM Resorts
Microchip
Micron
Microsoft
Mid-America Apartments
Moderna
Mohawk Industries
Molina Healthcare
Molson Coors
Mondelez International
Monolithic Power Systems
Monster Beverage
Moody's
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MSCI
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NetApp
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Newell Brands
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Nielsen
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Oneok
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Paramount
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Signature Bank
Simon
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Southern Company
Southwest Airlines
Stanley Black & Decker
Starbucks
State Street
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Synchrony
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Take-Two Interactive
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Whirlpool
Williams
Willis Towers Watson
Wynn Resorts
Xcel Energy
Xylem
Yum! Brands
Zebra
Zimmer Biomet
Zions Bancorp
Zoetis
import smart_open
def company_names_regex():
with smart_open.open(
"s3://wikipedia-meadowrun-demo/companies.txt"
) as companies_file:
companies = companies_file.read().splitlines()
return "|".join(companies)
import io
import tarfile
import time
import smart_open
from unzip_wikipedia_articles import iterate_articles_chunk
def convert_articles_chunk_to_tar_gz(articles_offset, num_articles):
"""
Reads the specified articles from the S3 bucket and writes them back to a tar.gz
file where the filenames in the tar archive are the titles and the contents of the
files are the contents of the articles.
"""
t0 = time.perf_counter()
with smart_open.open(
f"s3://wikipedia-meadowrun-demo/extracted-{articles_offset}.tar.gz", "wb"
) as raw_file, tarfile.TarFile(fileobj=raw_file, mode="w") as tar_file:
for title, text in iterate_articles_chunk(
"s3://wikipedia-meadowrun-demo/enwiki-latest-pages-articles-multistream-index.txt.bz2",
"s3://wikipedia-meadowrun-demo/enwiki-latest-pages-articles-multistream.xml.bz2",
articles_offset,
num_articles,
):
if text is not None and not text.startswith("#REDIRECT"):
bs = text.encode("utf-8")
ti = tarfile.TarInfo(title)
ti.size = len(bs)
with io.BytesIO(bs) as text_buffer:
tar_file.addfile(ti, text_buffer)
print(f"Processed {articles_offset}, {num_articles} in {time.perf_counter() - t0}")
import bz2
with bz2.open(
"enwiki-latest-pages-articles-multistream-index.txt.bz2", "rt", encoding="utf-8"
) as index_file:
i = 0
for _ in index_file:
i += 1
print(f"{i:,d} total articles")
[tool.poetry]
name = "meadowrun_hyperscan_demo"
version = "0.1.0"
description = ""
authors = ["Hyunho Richard Lee <hrichardlee@gmail.com>"]
[tool.poetry.dependencies]
python = "^3.9"
meadowrun = "0.1.11"
smart-open = {extras = ["s3"], version = "^6.0.0"}
pyre2 = "^0.3.6"
hyperscan = {version = "^0.3.2", platform = "linux"}
[tool.poetry.dev-dependencies]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
import tarfile
import smart_open
def iterate_extract(tar_file):
"""
Yields (article title, article content) for each article in the specified tar file
"""
with smart_open.open(
tar_file, "rb", compression="disable"
) as raw_file, tarfile.open(fileobj=raw_file, mode="r|gz") as tar_file_handle:
for member_file_info in tar_file_handle:
member_file_handle = tar_file_handle.extractfile(member_file_info)
if member_file_handle:
member_file_content = member_file_handle.read()
yield member_file_info.name, member_file_content
import asyncio
import itertools
import time
import meadowrun
from company_names import company_names_regex
from read_articles_extract import iterate_extract
def scan_hyperscan(extract_file, take_first_n, print_matches):
import hyperscan
i = 0
def on_match(match_id, from_index, to_index, flags, context=None):
nonlocal i
if i % 100000 == 0 and print_matches:
article_title, article_content = context
print(
article_title
+ ": "
+ str(article_content[from_index - 50 : to_index + 50])
)
i += 1
db = hyperscan.Database()
patterns = (
# expression, id, flags
(
company_names_regex().encode("utf-8"),
1,
hyperscan.HS_FLAG_CASELESS | hyperscan.HS_FLAG_SOM_LEFTMOST,
),
)
expressions, ids, flags = zip(*patterns)
db.compile(expressions=expressions, ids=ids, elements=len(patterns), flags=flags)
bytes_scanned = 0
t0 = time.perf_counter()
for article_title, article_content in itertools.islice(
iterate_extract(extract_file), take_first_n
):
db.scan(
article_content,
match_event_handler=on_match,
context=(article_title, article_content),
)
bytes_scanned += len(article_content)
time_taken = time.perf_counter() - t0
print(
f"Scanned {bytes_scanned:,d} bytes in {time_taken:.2f} seconds "
f"({bytes_scanned / time_taken:,.0f} B/s)"
)
async def scan_hyperscan_ec2():
return await meadowrun.run_function(
lambda: scan_hyperscan(
"s3://wikipedia-meadowrun-demo/extracted-200000.tar.gz", 10000, True
),
meadowrun.AllocCloudInstance("EC2"),
meadowrun.Resources(
logical_cpu=1,
memory_gb=2,
max_eviction_rate=80,
),
await meadowrun.Deployment.mirror_local(),
)
if __name__ == "__main__":
asyncio.run(scan_hyperscan_ec2())
import asyncio
import sys
import meadowrun
from scan_wikipedia_hyperscan import scan_hyperscan
async def scan_hyperscan_ec2_full():
total_articles = 22_114_834
chunk_size = 100_000
await meadowrun.run_map(
lambda i: scan_hyperscan(
f"s3://wikipedia-meadowrun-demo/extracted-{i}.tar.gz", sys.maxsize, False
),
[i * chunk_size for i in range(total_articles // chunk_size + 1)]
+ [i * chunk_size for i in range(total_articles // chunk_size + 1)],
meadowrun.AllocCloudInstance("EC2"),
meadowrun.Resources(
logical_cpu=1,
memory_gb=2,
max_eviction_rate=80,
),
await meadowrun.Deployment.mirror_local(),
num_concurrent_tasks=64,
)
if __name__ == "__main__":
asyncio.run(scan_hyperscan_ec2_full())
import asyncio
import itertools
import re
# import re2 as re
import time
import meadowrun
from company_names import company_names_regex
from read_articles_extract import iterate_extract
def scan_re(extract_file):
compiled_re = re.compile(company_names_regex(), re.IGNORECASE)
t0 = time.perf_counter()
bytes_scanned = 0
i = 0
for article_title, article_content in itertools.islice(
iterate_extract(extract_file), 100
):
for match in compiled_re.finditer(article_content.decode("utf-8")):
# print out a little context around a sample of matches
if i % 100000 == 0:
print(
f"{article_title}: "
+ " ".join(
match.string[match.start() - 50 : match.end() + 50].split()
)
)
i += 1
bytes_scanned += len(article_content)
time_taken = time.perf_counter() - t0
print(
f"Scanned {bytes_scanned:,d} bytes in {time_taken:.2f} seconds "
f"({bytes_scanned / time_taken:,.0f} B/s)"
)
async def scan_re_ec2():
return await meadowrun.run_function(
lambda: scan_re("s3://wikipedia-meadowrun-demo/extracted-200000.tar.gz"),
meadowrun.AllocCloudInstance("EC2"),
meadowrun.Resources(
logical_cpu=1,
memory_gb=2,
max_eviction_rate=80,
),
await meadowrun.Deployment.mirror_local(),
)
if __name__ == "__main__":
asyncio.run(scan_re_ec2())
import asyncio
import meadowrun
from convert_to_tar import convert_articles_chunk_to_tar_gz
async def unzip_all_articles():
total_articles = 22_114_834
chunk_size = 100_000
await meadowrun.run_map(
lambda i: convert_articles_chunk_to_tar_gz(i, chunk_size),
[i * chunk_size for i in range(total_articles // chunk_size + 1)],
meadowrun.AllocCloudInstance("EC2"),
meadowrun.Resources(
logical_cpu=1,
memory_gb=2,
max_eviction_rate=80,
),
await meadowrun.Deployment.mirror_local(),
num_concurrent_tasks=64,
)
if __name__ == "__main__":
asyncio.run(unzip_all_articles())
import time
from unzip_wikipedia_articles import iterate_articles_chunk
n = 1000
t0 = time.perf_counter()
bytes_read = 0
for title, text in iterate_articles_chunk(
"enwiki-latest-pages-articles-multistream-index.txt.bz2",
"enwiki-latest-pages-articles-multistream.xml.bz2",
0,
n,
):
bytes_read += len(title) + len(text)
print(
f"Read ~{bytes_read:,d} bytes from {n} articles in {time.perf_counter() - t0:.2f}s"
)
import bz2
import xml.etree.ElementTree
import smart_open
def _read_articles_in_section(data_file_section, article_ids):
"""
Reads articles from the given data file section, only returns articles that are in
articles_ids, and keeps trying to read until all article_ids have been read.
Assumes that data_file_section is a file-like object that contains content like:
<page>
<id>1234</id>
<title>Title Of Article</title>
<text>Text of article</text>
<page>
<page>
...
</page>
...
"""
while article_ids:
# we're only interested in seeing complete tags (i.e. the "end" of a tag)
parser = xml.etree.ElementTree.XMLPullParser(["end"])
current_article_title = None
current_article_should_be_returned = False
is_first_id = True
for line in data_file_section:
parser.feed(line)
done = False
for event_type, xml_node in parser.read_events():
if xml_node.tag == "id":
article_id = xml_node.text
if is_first_id:
# this is pretty hacky, we're just assuming that the page's id
# element comes before any other sub-ids
current_article_should_be_returned = article_id in article_ids
article_ids.discard(article_id)
is_first_id = False
elif xml_node.tag == "title":
current_article_title = xml_node.text
elif xml_node.tag == "text":
if current_article_should_be_returned:
# assumes there is an id and title tag before this tag, and
# those are the correct id/title tags
yield current_article_title, xml_node.text
elif xml_node.tag == "page":
done = True
if done:
# if we're done with the page element, we need to start a new XML parser
break
def iterate_articles_chunk(index_file, multistream_file, articles_offset, num_articles):
"""
Iterates through the articles in the wikipedia dump. Skips the first articles_offset
first articles, and iterates through num_articles
"""
with smart_open.open(
index_file, "rt", encoding="utf-8"
) as index_file, smart_open.open(
multistream_file, "rb", compression="disable"
) as compressed_data_file:
# Our strategy will be to group all the requested articles in each section, and
# then call _read_articles_in_section once for each section. These two variables
# will keep track of the "current" section
current_section_offset = None
current_article_ids = None
for i, line in enumerate(index_file):
# skip articles_offset lines
if i < articles_offset:
continue
# and stop when we get to articles_offset + num_articles lines
if i >= articles_offset + num_articles:
break
# parse the line
offset_string, sep, remainder = line[:-1].partition(":")
offset = int(offset_string)
article_id, sep, article_title = remainder.partition(":")
if current_section_offset == offset:
# if we're "in the same section", just add our article_id to the set
current_article_ids.add(article_id)
else:
# if we're in a new section, then call _read_articles_in_section on the
# previous section
if current_article_ids:
compressed_data_file.seek(current_section_offset)
with bz2.open(
compressed_data_file, "rt", encoding="utf-8"
) as data_file_section:
yield from _read_articles_in_section(
data_file_section, current_article_ids
)
# start keeping track of a new section
current_section_offset = offset
current_article_ids = {article_id}
# finally, if we have an unprocessed section, read that
if current_article_ids:
compressed_data_file.seek(current_section_offset)
with bz2.open(
compressed_data_file, "rt", encoding="utf-8"
) as data_file_section:
yield from _read_articles_in_section(
data_file_section, current_article_ids
)
aws s3 mb s3://wikipedia-meadowrun-demo
aws s3 cp enwiki-latest-pages-articles-multistream-index.txt.bz2 s3://wikipedia-meadowrun-demo
aws s3 cp enwiki-latest-pages-articles-multistream.xml.bz2 s3://wikipedia-meadowrun-demo
poetry run meadowrun-manage-ec2 grant-permission-to-s3-bucket wikipedia-meadowrun-demo
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