![]() ![]() As a result, it may be less up-to-date compared to other components in your stack. It's important to be aware that Optimus is currently under active development, and its last official release was in 2020. Moreover, Optimus includes processors designed to handle common real-world data types such as email addresses and URLs. These accessors make various tasks much easier to perform.įor example, you can sort a DataFrame, filter it based on column values, change data using specific criteria, or narrow down operations based on certain conditions. My intention is to decide later which data container and entry point is best, json (data entry via text editor) or SQLite (data entry via spreadsheet-like GUIs like SQLiteStudio). The data manipulation API in Optimus is like Pandas, but it offers more. I want to convert a JSON file I created to a SQLite database. You can load from and save back to Arrow, Parquet, Excel, various common database sources, or flat-file formats like CSV and JSON. Optimus can use Pandas, Dask, CUDF (and Dask + CUDF), Vaex, or Spark as its underlying data engine. ![]() Optimus is an all-in-one toolset designed to load, explore, cleanse, and write data back to various data sources. ON CONFLICT DO UPDATE SET Name=excluded.Name WHERE Name!=excluded.Cleaning and preparing data for DataFrame-centric projects can be one of the less enviable tasks. WITH RECURSIVE c(x) AS (VALUES(1) UNION ALL SELECT x+1 FROM c WHERE x>'appid', Value->'name' I generated a sample database file like this. Note that the performance on the query I was working to optimize, which isĪn production query for a clients, is about twice as fast. We use pip, a package manager bundled with Python, to download and install external libraries from PyPI. ![]() These libraries simplify code development by providing prewritten functions and tools. We are still a month away from feature-freeze Step 1: Install the Required Libraries In this step, we ensure that we have pandas and SQLAlchemy libraries installed in our Python environment. You'll notice that the JSON parser is quite a bit faster. .open x1.db CREATE TEMP TABLE appnames (AppID INTEGER PRIMARY KEY, Name TEXT) INSERT INTO appnames SELECT Value->'appid', Value->'name' FROM jsoneach ( (SELECT json FROM t1)) WHERE 1 ON CONFLICT DO UPDATE SET Nameexcluded.Name WHERE Nameexcluded. JSON Output Mode We can change the output mode like this. Here (temporarily - the link will be taken down at some point): We can also use SQLite functions like jsonobject () and/or jsonarray () to return query results as a JSON document. I concur that there is about a 16% performance reduction in the particular Seems unfortunate, but I'm guessing it's because the caching doesn't get used much so it just slows the parsing down in this case?Īdmittedly I'm going to get around to moving the JSON parsing out of SQL, so it's not like it'll eventually matter either way, but I decided it was worth mentioning my findings here. Testing it (with an in-memory DB and empty table, and an on-disk DB with the real table) shows about a 20% increase in time required for the query. The upsert was INSERT INTO app_names SELECT Value->'appid', Value->'name' FROM json_each(?) WHERE 1 ON CONFLICT DO UPDATE SET Name=excluded.Name WHERE Name!=excluded.Name The JSON being parsed is essentially and the table is defined as CREATE TABLE app_names (AppID INTEGER PRIMARY KEY, Name TEXT). I saw the new JSON changes got merged into the trunk and was hoping it might improve a big upsert I do, so I updated and gave it some tests. Consider using Python's built-in SQLite support as an alternative way of storing the data in a single file. ![]()
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