I'm tripping some codes for the check engine light and took my car in to get repaired. They did a smoke test and are fairly certain that I need to replace a valve on the canister (there are others that aren't on the canister). The said that the dealer told them I needed to replace the whole canister because it is attached to the valve. Based on some of the documents I had the repair shop send me, I saw this part number 9091012264. That part looks like it can be ordered separately.


Is there a diagram where I can see the parts in detail? 
Is that the valve on the canister?



Question: Kyoto Cabinet / Berkeley DB : Hash table size limitations
Answer: Below is how I managed to store billions of records despite the writing limitations encountered with KC.

With much effort, I still haven't solved the problem for neither Kyoto Cabinet nor Berkeley DB. However I came up with an interesting workaround using Kyoto Cabinet.

I noticed I cannot write more than 25M records on one KC file, but read has no such limitation −it is always fast, regardless of the size of the database. The solution I found is to create a new KC file (a new database) for every 25M new records. That way the reading happens on many KC files and is still fast, and the writing happens only on the last created file and is fast as well. Only remaining problem was to allow update/deletion of the records on the previous files. For that, I copied the SSTables approach, which is :


All the 0 to N-1 files are read-only, file N is read+write.
Any insert/update/deletion is written in file N.
Reads look into files N to 0, and return the first-seen/last-written insertion/update/deletion.
A bloom filter is attached to each file to avoid accessing a file that doesn't have the wanted record.
As soon as file N reaches 25M records, it becomes read-only and file N+1 is created.


Notes :


Just like with SSTables, If a lot of updates/deletions are performed, we might want to perform compaction. However contrary to SSTables, compaction here doesn't require to rewrite the file. Outdated records are simply removed from the KC files, and if a KC file gets very small, it can be either removed −reinserting the records in file N− or reopenned for new insertions −provided the next files are compact.
A deletion does not delete the record, but write a special value that identifies the record as deleted. During compaction, deleted records are removed for real.
Checking if a record exists usually requires to look into the database. Thanks to the bloom filters, most of the negative answers can be given without any disk access.