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The potential compression ratio can easily exceed 10:1, so you could be dealing with 52GiB or more of raw data in each of those weeklyh files.
#Creating a table in sequel pro zip
7z file format is a compression+container format similar to zip or tar+gzip (tgz) that supports one of the highest compression ratio lossless compression formats in common use, LZMA2, among others.Īlmost certainly, the file contains a heavily-compressed copy of the output from mysqldump of your database, either with a single file, a file for each schema, or a file for each table, depending on how they're making the backup. and if you examine the content of the files, the reason for this should become apparent. However, I think that learning some common SQL functions can come a long way inīeing able to make your database do the work.What you do depends heavily on what you're wanting the end result to be, but there is almost certainly not a way to do what you are thinking of. Just do the transformations in Ruby, as we know Ruby much better than SQL. Might require more complex data transformations. This article showed a fairly simple scenario, in many cases our data migrations
#Creating a table in sequel pro code
Strategy, and the code wasn’t any more complex than the other strategies. Inserting from a dataset was by far the fastest We’ve compared the performance of migrating records (a) individually, (b) inīulk, and (c) from a dataset. I love that Sequel allows me to do anything I want Ways to make the database do the majority of the work I need, especially since Closing thoughtsįor the past several years, I’ve become more and more interested in finding ItĪllocates zero additional memory on the application side, because everything is In our case it was 4x faster than the bulk insert strategy. Inserting from a dataset performed the best, since there the database does all
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Surprised that it performs at least as fast as the non-batched version. Imagined that we’re trading off speed for reduced load.
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To see that prepared statements provided a ~33% speedup, though at a costįor bulk inserts, I expected the batching variant to be slower, because I I was aīit surprised to see that it’s allocating significantly more memory than theīulk insert strategy, but I believe it’s because we’re allocating hashes forĮach record, while with bulk inserts we’re allocating value arrays. Memory allocation of all 5 migration strategies we’ve talked about (database isĪs expected, inserting records individually is the slowest strategy. I’ve created a script which populates the activity_logs table with 100,000Īpproval logs and 100,000 publication logs, and measures execution time and alter_table :activity_logs do drop_column :event # this table will only hold approval logs now drop_column :target # this was specific to publication logs set_column_not_null :user_id # only publication logs didn't have user id set end Measuring performance In PostgreSQL, the syntax looks like this:ĭB. Most SQL databases support inserting multiple records in a single query, which Since long-running migrations can generally be problematic, let’s find a better It so happens ours was a logs table with lots of records (about 200,000 IIRC). This strategy would usually perform well enough on small to medium tables, but call ( playlist_id: log, action: log, target: log, created_at: log, ) end # delete records from the old table publication_logs. prepare :insert, :insert_publication_data, playlist_id: : $playlist_id, action: : $action, target: : $target, created_at: : $created_at # insert each record individually into the new table publication_logs. where ( event: "publication" ) prepared_insert = DB. # select records we want to move publication_logs = DB.