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🔗 Feature: Large Rock Store

🔗 Definitions

🔗 Design Goals


🔗 Database file structure

The database file starts with the usual header slot. Database entry slots follow. All slots are of the same size (e.g., 16KB or 32KB).


Each slot may be one of two types detailed below: header slot and data slot.

🔗 header slot

db size and other aggregate meta data about the db
stored db map size
stored db map pointer

The header should be similar to the Small Rock header. The db map fields are not going to be used in the initial implementation, but may be used to indicate presence and describe the location of the db index in future code revisions.

🔗 data slot

StoreEntry key
First slot pointer in the entry chain
Next slot pointer in the entry chain
Entry chain version
StoreEntry embryo (STORE_META_STD)
raw entry data

A single cached entry may have multiple db slots, forming an “entry chain”.

First slot pointer field contains the address of the first slot in the entry chain. This is used to find the entry which should be purged to free a slot needed for a more valuable entry (Rock hashes entry keys to slot addresses). The first slot in the entry chain will point either to itself or to the last chain slot (XXX).

Next slot pointer field contains the address of the next slot in the entry chain.

The entry version is incremented every time an entry is overwritten. It is OK for the counter to wrap. The version is used to detect “dangling” slots during db map validation. Dangling slots no longer belong to any stored entry but are not marked as empty either (e.g., due to worker or disker crashes). The entry chain version is the version of entry’s first slot. If an entry chain contains a slot with a different entry chain version, the entire chain is invalid and all its slots are marked as empty during db validation process.

The StoreEntry embryo (STORE_META_STD) is used to create a StoreEntry object for a given slot when hit is detected. XXX: research whether we have to store that information or can delay using embryo data until the entire first hit slot is loaded.

The Weight field might be used by an LFU-like replacement policy. When two entry chains compete for the same db slot, an entry with the higher weight wins (but loses some of its weight). XXX: Detail weight calculation algorithm and, hence, the replacement policy. XXX: Consider using LRU instead.

All slot pointers are 4-byte (32-bit) unsigned integers, with zero indicating a nil pointer. Even a 2TB db with 8KB slots needs just 28 bits to enumerate all possible 268M slots.

🔗 Db map

The map is an array of fixed-size items. The item position in the array corresponds to a slot position in the database. Store keys are hashed to find the corresponding item position (this design is similar to what Small Rock is using). Each item contains the non-data portion of the corresponding data slot plus atomic locks and related state members.

To lock an entry chain, lock the map item corresponding to the first chain slot. There is no need to lock each individual map item (corresponding to all slots in the chain).

🔗 Design decisions

Several alternative design options have been considered and either rejected or postponed. Some of them are documented here.

  1. Initial implementation will scan the entire db to build an in-memory cache_dir map (StoreMap). Eventually, we will start saving the map to disk and loading it at startup to decrease db loading times. We need more information on db loading times, which may affect how the db map is stored and loaded.
  2. It is possible to support a hashing algorithm that does not rely on map item to slot correspondence. Such an algorithm will need a map that stores “current” or “this” slot pointers in addition to previous and next pointers. It will help avoid persistent collisions (two popular cache entries always evicting each other). However, implementing such an algorithm in shared memory with optimistic atomic locks is difficult. We want to get a simpler implementation working first.
  3. We could group db slot pointers into inodes, indirect-nodes, and doubly-indirect-nodes like traditional file systems do. This would allow us to search for a particular offset within an entry much faster, without scanning the entry chain, one map item at a time. This optimization should be considered when the hashing algorithm stabilizes (as it will affect whether we need pointers from slots to entries when finding a victim to purge).
  4. In configurations where most cached entries are larger than one slot, it is possible to save RAM and map I/O by splitting the map into two: a “next slot” map (version number plus just one or two 4-byte pointers per slot) and an inode map (one larger item per cached entry with all the entry metadata). This optimization should be considered when the map structure and hashing algorithm stabilizes, especially if there are cases where there is at least an order of magnitude a difference between the number of db slots and the number of cached entries (in a full db).

🔗 Slow cache index build

Rock caches usually build their in-RAM index slower than UFS caches do:

Rock: 2016/09/13 00:25:34|   Took 1498.42 seconds (3972.24 objects/sec).
UFS:  2016/09/13 00:00:51|   Took 5.71 seconds (533481.90 objects/sec).

Longer index build times are a side effect of design decision #1 discussed above. There are several important wrinkles here:

  1. Squid start itself is not slow. Cache index build is slow.
  2. Squid can serve requests, including cache hits while it builds rock index, but indexing does affect overall Squid performance and hit ratios.
  3. Avoid comparing loading a “few” UFS entries (from the clean swap state) with scanning all available cache slots for rock. The biggest difference is observed for a virtually empty UFS cache that was in use for a short time (small swap.state). Rock focus is on Squid running for a long time with a full cache (the common and intended use case).
  4. We are essentially comparing a from-scratch index build for rock with a clean index loading for UFS. If you remove all swap state files, UFS indexing time will probably be worse than that of rock. If you leave dirty swap state files, then UFS indexing may slow down significantly; this happens after Squid crashes, for example. Rock indexing does not depend on the previous Squid state.
  5. Rock indexing code can and probably will be optimized in various ways, of course. Many trade offs are involved, and some optimizations may hurt runtime performance. For example, there is a trade-off between
    • maintaining a disk index (i.e., swap state files) at runtime (and then saving a clean index at shutdown) like UFS stores do and
    • building an index from scratch by scanning the entire cache at start like rock stores do.

Categories: Feature

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