I've got a problem i have been trying to work out I have googled a few things that are similar to what i want to do but can't work out exactly how to do it,
I have around 250M ip address's and i want to look that up against the maxmind geolite2 data so that i can tell what country each IP-address originates from,
I have imported all the data into my Redshift cluster with talend,
table a has ID and 'ipaddress' ie 10.0.0.5
table b (maxmind) has country name and IP range as expressed as 10.0.0.0/24
how could i use Redshift SQL to match these two considering the size of my source data source?
edit: heres the link to the geolite2 data https://dev.maxmind.com/geoip/geoip2/geolite2/
You could try using Amazon Redshift's ability to Import Custom Python Library Modules to load the netaddr library. Then, you could use the library within a User Defined Function written in Python.
See also: IP Range to CIDR conversion in Python?
Related
Is there an alternative to GeoIPCountryWhois.csv available? Since, Max Mind has upgraded to geoip2 databases this file is not shipped anymore.
I am trying to use zmap/zgrab on this list of start/end IPs with the country code and do same data analysis on output from zgrab.
I am importing several files from Google Cloud Storage (GCS) through Google DataPrep and store the results in tables of Google BigQuery. The structure on GCS looks something like this:
//source/user/me/datasets/{month}/2017-01-31-file.csv
//source/user/me/datasets/{month}/2017-02-28-file.csv
//source/user/me/datasets/{month}/2017-03-31-file.csv
We can create a dataset with parameters as outlined on this page. This all works fine and I have been able to import it properly.
However, in this BigQuery table (output), I have no means of extracting only rows with for instance a parameter month in it.
How could I therefore add these Dataset Parameters (here: {month}) into my BigQuery table using DataPrep?
While the original answers were true at the time of posting, there was an update rolled out last week that added a number of features not specifically addressed in the release notes—including another solution for this question.
In addition to SOURCEROWNUMBER() (which can now also be expressed as $sourcerownumber), there's now also a source metadata reference called $filepath—which, as you would expect, stores the local path to the file in Cloud Storage.
There are a number of caveats here, such as it not returning a value for BigQuery sources and not being available if you pivot, join, or unnest . . . but in your scenario, you could easily bring it into a column and do any needed matching or dropping using it.
NOTE: If your data source sample was created before this feature, you'll need to create a new sample in order to see it in the interface (instead of just NULL values).
Full notes for these metadata fields are available here:
https://cloud.google.com/dataprep/docs/html/Source-Metadata-References_136155148
There is currently no access to data source location or parameter match values within the flow. Only the data in the dataset is available to you. (except SOURCEROWNUMBER())
Partial Solution
One method I have been using to mimic parameter insertion into the eventual table is to have multiple dataset imports by parameter and then union these before running your transformations into a final table.
For each known parameter search dataset, have a recipe that fills a column with that parameter per dataset and then union the results of each of these.
Obviously, this is only so scalable i.e. it works if you know the set of parameter values that will match. once you get to the granularity of time-stamp in the source file there is no way this is feasible.
In this example just the year value is the filtered parameter.
Longer Solution (An aside)
The alternative to this I eventually skated to was to define dataflow jobs using Dataprep, use these as dataflow templates and then run an orchestration function that ran the dataflow job (not dataprep) and amended the parameters for input AND output via the API. Then there was a transformation BigQuery Job that did the roundup append function.
Worth it if the flow is pretty settled, but not for adhoc; all depends on your scale.
I have a csv files, that it has the following structure.
ERP,J,JACKSON,8388 SOUTH CALIFORNIA ST.,TUCSON,AZ,85708,267-3352,,ALLENTON,MI,48002,810,710-0470,369-98-6555,462-11-4610,1953-05-00,F,
MARKETING,J,JACKSON,8388 SOUTH CALIFORNIA ST.,TUCSON,AZ,85708,267-3352,,ALLENTON,MI,48002,810,710-0470,369-98-6555,462-11-4610,1953-05-00,F,
As you can see there is not header, but for your information the first part (first column) represents the sector where are getting the data.
What I have to do is depending on the first column value, for example (MARKETING or ERP) I have to send all that rows to a different output directory.
For example, all rows with ERP to /output/ERP/
all rows with MARKETING to /output/marketing/
I have an idea about how to do it, but my problem is about the RouteOnAttribute processor I am using, I don't know how to refer to the first column and to indicate what is the value (ERP or MARKETING) to later on send it to the correct output directory.
Here is my schema.
Thanks.
Use PartitionRecord processor for this case.
Configure the processor with record reader/writer controller services. Even though if you are not having header you can use col1,col2...etc in avro schema.
add new property that defines processor to use that field for partition the flowfile.
Now partition record processor adds the partition field attribute with value, by making use of this attribute value we can dynamically store files into respected directories dynamically.
Flow:
1.GetFile
2.PartitionRecord
3.PutFile //configure directory as /output/${<keep_partition_field_name_here>}
Please refer this link for configuring usage of partition record processor.
(or)
Old Approach:
Using RouteText processor instead of SplitText + RouteOnAttribute Processors
Configure RouteText processor as
Use the ERP/MARKETING connections connect to PutFile processor and use RouteText.Route attribute value to dynamically save the files into Directories.
Flow:
1.GetFile
2.RouteText
3.PutFile //configure directory as /output/${RouteText.Route}/
You can also use Group Regular expression property value to create partitions.
Note
Using PartitionRecord processor will be more efficient than RouteText processor.
I need to run a join query on BigQuery of one project, that may return large amount of data (that may not fit in VM's memeory), and then save the results in the BigQuery of another project.
Is there an easy way to do this without loading the data in VM, as data size can vary and VM may not have enough memory to load it?
One method is to bypass the VM for the operation and utilize Google Cloud Storage instead.
The process will look like following
Create a GS bucket that both projects has access to
Source project - Export the table to the GS bucket (this is possible from the web interface, pretty sure the CLI tools can do it to)
Destination project - Create a new table from the files in the GS bucket
to save result of query to a table in any project - you do not need to save it first to VM you should just set properly destination property and of course you need to have write permissions to dataset that contain that table!
Destination property can vary depend on client tool you use
for example, if you are using REST API's jobs.insert you should set below property
configuration.query.destinationTable nested object [Optional]
Describes the table where the query results should be stored. If not
present, a new table will be created to store the results. This
property must be set for large results that exceed the maximum
response size.
configuration.query.destinationTable.datasetId string [Required]
The
ID of the dataset containing this table.
configuration.query.destinationTable.projectId string [Required]
The
ID of the project containing this table.
configuration.query.destinationTable.tableId string [Required]
The ID
of the table. The ID must contain only letters (a-z, A-Z), numbers
(0-9), or underscores (_). The maximum length is 1,024 characters.
End goal is to find all used addresses of a xpub programmatically, in batches, by querying a local node.
This PHP tool ( https://github.com/dan-da/hd-wallet-addrs ) extracts regular bitcoin addresses from an HD bitcoin wallet . I have to query a local bitcoin node to find out whether the extracted addresses have been used or not.This can easily be found out by querying the node one address at a time ( https://bitco.in/en/developer-reference#getreceivedbyaddress ). It works, but is too slow.
How can the same be done in batches ? i.e is there a Core function call or something to check info about a group of addresses ?
or any other way this can be accomplished ? I am relatively new to bitcoin and don't fully understand its inner workings.
P.S: can't use an external API like blockchain.info
With importAddress and rescan (3 paramter) set to true, you can add the addresses that you want watch.
Than with listTransactions you can list the last transactions that affect the imported address.