![]() SELECT string FROM string_collection WHERE lower(string) LIKE ‘o%’ Ī performance comparison of LIKE with lower vs ILIKE can be read here. The same effect can be achieved using the ‘lower()’ function and LIKE of PostgreSQL. ILIKE is similar to LIKE in all aspects except in one thing: it performs a case in-sensitive matching: SELECT string FROM string_collection WHERE string ILIKE 'O%' It is also possible to combine _ and % to get the desired result: SELECT string FROM string_collection WHERE string LIKE '_n%' In plain English, “It matches all strings of three characters whose middle character is small letter n”. įollowing query illustrate the use of _: SELECT string FROM string_collection WHERE string LIKE '_n_' It matches all strings beginning with ‘O’. _ in a pattern matches any single character.įor instance, consider the following query: SELECT string FROM string_collection WHERE string LIKE 'O%' % sign in a pattern matches any sequence of zero or more characters. Two of the important selectors in pattern matching with LIKE/ILIKE are the percentage sign(%) and underscore(_). To begin with, we will create a tiny table with few random string values. LIKE is the SQL standard while ILIKE is a useful extension made by PostgreSQL. The intermediate states can be stored in druid too.Īnd visualization would be with apache superset.LIKE and ILIKE are used for pattern matching in PostgreSQL. Get the data with Kafka or with native python, do the first processing, and store data in Druid, the second processing will be done with Apache Spark getting data from apache druid. sometimes I may get a different format of data.Everything is done with vanilla python and Pandas.I make a report based on the two files in Jupyter notebook and convert it to HTML. The next process is making a heavy computation in a parallel fashion (per partition), and storing 3 intermediate versions as parquet files: two used for statistics, and the third will be filtered and create the final files. I have a script that does some cleaning and then stores the result as partitioned parquet files because the following process cannot handle loading all data to memory. My process is like this: I would get data once a month, either from Google BigQuery or as parquet files from Azure Blob Storage. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product. This provides our data scientist a one-click method of getting from their algorithms to production. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. That requires serving layer that is robust, agile, flexible, and allows for self-service. We have dozens of data products actively integrated systems. ![]() The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see ).Īt Stitch Fix, algorithmic integrations are pervasive across the business. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.īeyond data movement and ETL, most #ML centric jobs (e.g. ![]() We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. Apache Spark on Yarn is our tool of choice for data movement and #ETL. We store data in an Amazon S3 based data warehouse. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. The algorithms and data infrastructure at Stitch Fix is housed in #AWS.
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