A datalake is an archive that contains files and metadata records about those files. The datalake project consists of a number of pieces:
The ingester that listens for new files pushed to the datalake and ingests their metadata so it can be searched.
The api to query over the files in the datalake.
The client, which is a python and command-line interface to the datalake. You can use it to push files to the datalake, list the files available in the datalake, and retrieve files from the datalake.
To use this client, you (or somebody on your behalf) must be operating an instance of the datalake-ingester and the datalake-api. You will need some configuration information from them.
Why would I use this? Because you just want to get all of the files into one place with nice uniform metadata so you can know what is what. Then you can pull the files onto your hardrive for your grepping and awking pleasure. Or perhaps you can feed them to a compute cluster of some sort for mapping and reducing. Or maybe you don't want to set up and maintain a bunch of log ingestion infrastructure, or you don't trust that log ingestion infrastructure to be your source of truth. Or maybe you just get that warm fuzzy feeling when things are archived somewhere.
pip install datalake
If you plan to use the queuing feature, you must install some extra dependencies:
apt-get install libffi-dev # or equivalent
pip install datalake[queuable]
datalake needs a bit of configuration. Every configuration variable can either
be set in /etc/datalake.conf, set as an environment variable, or passed in as
an argument. For documentation on the configuration variables, invoke datalake --help.
datalake has a python API and a command-line client. What you can do with one, you can do with the other. Here's how it works:
Push a log file:
datalake push --start 2015-03-20T00:05:32.345Z
--end 2015-03-20T23:59.114Z \
--where webserver01 --what nginx /path/to/nginx.log
Push a log file with a specific work-id:
datalake push --start 2015-03-20T00:00:05:32.345Z \
--end 2015-03-20T00:00:34.114Z \
--what blappo-etl --where backend01 \
--work-id blappo-14321359
The work-id is convenient for tracking processing jobs or other entities that may pass through many log-generating machines as they proceed through life. It must be unique within the datalake. So usually some kind of domain-specific prefix is recommended here.
List the syslog and foobar files available from webserver01 since the specified start date.
datalake list --where webserver01 --start 2015-03-20 --end `date -u` \
--what syslog,foobar
Fetch the blappo gather, etl, and cleanup log files with work id blappo-14321359:
datalake fetch --what gather,etl,cleanup --work-id blappo-14321359
make docker test
Files that are shipped to the datalake are accompanied by a JSON metadata document. Here it is:
{
"version": 0,
"start": 1426809920345,
"end": 1426895999114,
"path": "/var/log/syslog.1"
"work_id": null,
"where": "webserver02",
"what": "syslog",
"id": "6309e115c2914d0f8622973422626954",
"hash": "a3e75ee4f45f676422e038f2c116d000"
}
version: This is the metadata version. It should be 0.
start: This is the time of the first event in the file in milliseconds since the epoch. Alternatively, if the file is associated with an instant, this is the only relevant time. It is required.
end: This is the time of the last event in the file in milliseconds since the
epoch. If the key is not present or if the value is None, the file represents a
snapshot of something like a weekly report where only one date (start) is
relevant.
path: The absolute path to the file in the originating filesystem.
where: This is the location or server that generated the file. It is required and must only contain lowercase alpha-numeric characters, - and _. It should be concise. 'localhost' and 'vagrant' are bad names. Something like 'whirlyweb02-prod' is good.
what: This is the process or program that generated the file. It is required and must only contain lowercase alpha-numeric characters, - and _. It must not have trailing file extension (e.g., .log). The name should be concise to limit the chances that it conflicts with other whats in the datalake. So names like 'job' or 'task' are bad. Names like 'balyhoo-source-audit' or 'rawfood-ingester' are good.
id: An ID for the file assigned by the datalake. It is required.
hash: A 16-byte blake2 hash of the file content. This is calcluated and assigned by the datalake. It is required.
work_id: This is an application-specific id that can be used later to retrieve the file. It is required but may be null. In fact the datalake utilities will generally default it to null if it is not set. It must not be the string "null". It should be prepended with a domain-specific prefix to prevent conflicts with other work id spaces. It must only contain lowercase alpha-numeric characters, -, and _.
In practice, metadata is stored in DynamoDB, which has strict but simple rules about defining and querying indexes. We wish to support a few simple queries over our metadata:
To achieve this using DynamoDB, we adopt the notion of "time buckets," each of which is one day long. So a file whose data spans the period of today from 1:00-2:00 would have a single record in today's time bucket. A file whose data spans the period from yesterday at noon to today at noon has two records: one in yesterday's bucket and one in today's bucket. And so when a user queries over a time period, we simply calculate the buckets that the time period spans, then look in each bucket for relevant files.
But doesn't that mean we have to sometimes write multiple records per file? Yes. What if a file spans 100 days? Do we really want to put a record in each of 100 buckets? Well, this would be a pretty uncommon case for the uses that we are envisioning. In practice, such files should be broken up into smaller files and uploaded more frequently. What if a user queries for 100 days worth of data? Well, we examine a bunch of buckets and it takes a while. Users that are not prepared to wait this long should make smaller requests.
To enable these queries, we have two hash-and-range indexes. They have the following HASHKEY RANGEKEY format:
TIME_BUCKET:WHAT WHERE:ID
WORK_ID:WHAT WHERE:ID
The first index is to support query types 1 and 2. By using TIME_BUCKET:WHAT as the hash key we prevent "hot" hash keys by distributing writes and queries across WHATs. So while all the records for a day will be written to the same TIME_BUCKET, and while users are much more likely to query recent things from the last few TIME_BUCKETs, we spread the load across a diversity of WHATs. The WHERE:ID range key can be used to retrieve a subset of WHEREs if necessary. Finally, we append the file ID to ensure that the key is unique as required by DynamoDB.
The second index supports query types 3 and 4 and follows a pattern similar to the first. However, it should be noted that the WORK_ID is optional metadata, but required for indexing purposes. To work around this without introducing a hot hash key in the second index, the ingester generates a random WORK_ID with the reserved prefix "null".
The datalake client specifies metadata that is recorded when a file is pushed to the datalake. We need to store some administrative fields to get our queries to work with the dynamodb indexes. These records have the following format:
{
"version": 0,
"url": "s3://datalake/d-nebraska/nginx/1437375600000/91dd2525a5924c6c972e3d67fee8cda9-nginx-523.txt",
"time_index_key": "16636:nginx",
"work_id_index_key": "nullc177bfc032c548ba9e056c8e8672dba8:nginx",
"range_key": "nebraska:91dd2525a5924c6c972e3d67fee8cda9",
"create_time": 1426896791333,
"size": 7892341,
"metadata": { ... },
}
version: the version of the datalake record format. What we describe here is version 0.
url: the url of the resource to which the datalake record pertains.
time_index_key: the hash key for the index used for time-based queries. It is formed by joining the "time bucket" number and the "what" from the metadata.
work_id_index_key: the hash key for the index used for work_id-based queries. It is formed by joining the work_id and the "what" from the metadata. Note that if the work_id is null, a random work_id will be generated to prevent ingestion failures and hot hash keys. Of course in this case retrieving by work_id is not meaninful or possible.
range_key: the range key used by the time-based and work_id-based indexes. It is formed by joining the "where" and the "id" from the metadata.
create_time: the creation time of the file in the datalake
size: the size of the file in bytes
The datalake-ingester ingests datalake metadata records into a database so that they may be queried by other datalake components.
The ingester looks something like this:
+----------+ +---------+
+-------+ +-----------------+ | |---->| storage |
-->| queue |--->| s3_notification |--->| ingester | +---------+
+-------+ +-----------------+ | |--+
+----------+ | +----------+
+->| reporter |
+----------+
A queue receives notice that an event has occured in the datalake's s3 bucket. An s3_notification object translates the event from the queue's format to the datalake record format (see above). Next the ingester updates the storage (i.e., dynamodb) and reports the ingestion status to the reporter (i.e., SNS).
The datalake ingester emits a Datalake Ingester Report for each file that it ingests. The report has the following format:
{
"version": 0,
"status": "success",
"start": 1437375854967,
"duration": 0.738383,
"records": [
{
"url": "s3://datalake/d-nebraska/nginx/1437375600000/91dd2525a5924c6c972e3d67fee8cda9-nginx-523.txt",
"metadata": { ... }
}
]
}
version: the version of the datalake ingester report format. What we describe here is version 0.
status: Either "success", "warning", or "error" depending on how successful ingestion was. If status is not "success" expect "message" to be set with a human-readable explanation.
start: ms since the epoch when the ingestion started.
duration: time in seconds that it took to ingest the record.
records: a list of records that were ingested. Note that this is typically a list with one element. However, some underlying protocols (e.g., s3 notifications) may carry information about multiple records. Under these circumstances multiple records may appear.
The datalake-api offers and HTTP interface to the datalake.
FAQs
datalake: a metadata-aware archive
We found that datalake demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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