Summary:
Adds a new Cache::ApplyToAllEntries API that we expect to use
(in follow-up PRs) for efficiently gathering block cache statistics.
Notable features vs. old ApplyToAllCacheEntries:

* Includes key and deleter (in addition to value and charge). We could
have passed in a Handle but then more virtual function calls would be
needed to get the "fields" of each entry. We expect to use the 'deleter'
to identify the origin of entries, perhaps even more.
* Heavily tuned to minimize latency impact on operating cache. It
does this by iterating over small sections of each cache shard while
cycling through the shards.
* Supports tuning roughly how many entries to operate on for each
lock acquire and release, to control the impact on the latency of other
operations without excessive lock acquire & release. The right balance
can depend on the cost of the callback. Good default seems to be
around 256.
* There should be no need to disable thread safety. (I would expect
uncontended locks to be sufficiently fast.)

I have enhanced cache_bench to validate this approach:

* Reports a histogram of ns per operation, so we can look at the
ditribution of times, not just throughput (average).
* Can add a thread for simulated "gather stats" which calls
ApplyToAllEntries at a specified interval. We also generate a histogram
of time to run ApplyToAllEntries.

To make the iteration over some entries of each shard work as cleanly as
possible, even with resize between next set of entries, I have
re-arranged which hash bits are used for sharding and which for indexing
within a shard.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/8225

Test Plan:
A couple of unit tests are added, but primary validation is manual, as
the primary risk is to performance.

The primary validation is using cache_bench to ensure that neither
the minor hashing changes nor the simulated stats gathering
significantly impact QPS or latency distribution. Note that adding op
latency histogram seriously impacts the benchmark QPS, so for a
fair baseline, we need the cache_bench changes (except remove simulated
stat gathering to make it compile). In short, we don't see any
reproducible difference in ops/sec or op latency unless we are gathering
stats nearly continuously. Test uses 10GB block cache with
8KB values to be somewhat realistic in the number of items to iterate
over.

Baseline typical output:

```
Complete in 92.017 s; Rough parallel ops/sec = 869401
Thread ops/sec = 54662

Operation latency (ns):
Count: 80000000 Average: 11223.9494  StdDev: 29.61
Min: 0  Median: 7759.3973  Max: 9620500
Percentiles: P50: 7759.40 P75: 14190.73 P99: 46922.75 P99.9: 77509.84 P99.99: 217030.58
------------------------------------------------------
[       0,       1 ]       68   0.000%   0.000%
(    2900,    4400 ]       89   0.000%   0.000%
(    4400,    6600 ] 33630240  42.038%  42.038% ########
(    6600,    9900 ] 18129842  22.662%  64.700% #####
(    9900,   14000 ]  7877533   9.847%  74.547% ##
(   14000,   22000 ] 15193238  18.992%  93.539% ####
(   22000,   33000 ]  3037061   3.796%  97.335% #
(   33000,   50000 ]  1626316   2.033%  99.368%
(   50000,   75000 ]   421532   0.527%  99.895%
(   75000,  110000 ]    56910   0.071%  99.966%
(  110000,  170000 ]    16134   0.020%  99.986%
(  170000,  250000 ]     5166   0.006%  99.993%
(  250000,  380000 ]     3017   0.004%  99.996%
(  380000,  570000 ]     1337   0.002%  99.998%
(  570000,  860000 ]      805   0.001%  99.999%
(  860000, 1200000 ]      319   0.000% 100.000%
( 1200000, 1900000 ]      231   0.000% 100.000%
( 1900000, 2900000 ]      100   0.000% 100.000%
( 2900000, 4300000 ]       39   0.000% 100.000%
( 4300000, 6500000 ]       16   0.000% 100.000%
( 6500000, 9800000 ]        7   0.000% 100.000%
```

New, gather_stats=false. Median thread ops/sec of 5 runs:

```
Complete in 92.030 s; Rough parallel ops/sec = 869285
Thread ops/sec = 54458

Operation latency (ns):
Count: 80000000 Average: 11298.1027  StdDev: 42.18
Min: 0  Median: 7722.0822  Max: 6398720
Percentiles: P50: 7722.08 P75: 14294.68 P99: 47522.95 P99.9: 85292.16 P99.99: 228077.78
------------------------------------------------------
[       0,       1 ]      109   0.000%   0.000%
(    2900,    4400 ]      793   0.001%   0.001%
(    4400,    6600 ] 34054563  42.568%  42.569% #########
(    6600,    9900 ] 17482646  21.853%  64.423% ####
(    9900,   14000 ]  7908180   9.885%  74.308% ##
(   14000,   22000 ] 15032072  18.790%  93.098% ####
(   22000,   33000 ]  3237834   4.047%  97.145% #
(   33000,   50000 ]  1736882   2.171%  99.316%
(   50000,   75000 ]   446851   0.559%  99.875%
(   75000,  110000 ]    68251   0.085%  99.960%
(  110000,  170000 ]    18592   0.023%  99.983%
(  170000,  250000 ]     7200   0.009%  99.992%
(  250000,  380000 ]     3334   0.004%  99.997%
(  380000,  570000 ]     1393   0.002%  99.998%
(  570000,  860000 ]      700   0.001%  99.999%
(  860000, 1200000 ]      293   0.000% 100.000%
( 1200000, 1900000 ]      196   0.000% 100.000%
( 1900000, 2900000 ]       69   0.000% 100.000%
( 2900000, 4300000 ]       32   0.000% 100.000%
( 4300000, 6500000 ]       10   0.000% 100.000%
```

New, gather_stats=true, 1 second delay between scans. Scans take about
1 second here so it's spending about 50% time scanning. Still the effect on
ops/sec and latency seems to be in the noise. Median thread ops/sec of 5 runs:

```
Complete in 91.890 s; Rough parallel ops/sec = 870608
Thread ops/sec = 54551

Operation latency (ns):
Count: 80000000 Average: 11311.2629  StdDev: 45.28
Min: 0  Median: 7686.5458  Max: 10018340
Percentiles: P50: 7686.55 P75: 14481.95 P99: 47232.60 P99.9: 79230.18 P99.99: 232998.86
------------------------------------------------------
[       0,       1 ]       71   0.000%   0.000%
(    2900,    4400 ]      291   0.000%   0.000%
(    4400,    6600 ] 34492060  43.115%  43.116% #########
(    6600,    9900 ] 16727328  20.909%  64.025% ####
(    9900,   14000 ]  7845828   9.807%  73.832% ##
(   14000,   22000 ] 15510654  19.388%  93.220% ####
(   22000,   33000 ]  3216533   4.021%  97.241% #
(   33000,   50000 ]  1680859   2.101%  99.342%
(   50000,   75000 ]   439059   0.549%  99.891%
(   75000,  110000 ]    60540   0.076%  99.967%
(  110000,  170000 ]    14649   0.018%  99.985%
(  170000,  250000 ]     5242   0.007%  99.991%
(  250000,  380000 ]     3260   0.004%  99.995%
(  380000,  570000 ]     1599   0.002%  99.997%
(  570000,  860000 ]     1043   0.001%  99.999%
(  860000, 1200000 ]      471   0.001%  99.999%
( 1200000, 1900000 ]      275   0.000% 100.000%
( 1900000, 2900000 ]      143   0.000% 100.000%
( 2900000, 4300000 ]       60   0.000% 100.000%
( 4300000, 6500000 ]       27   0.000% 100.000%
( 6500000, 9800000 ]        7   0.000% 100.000%
( 9800000, 14000000 ]        1   0.000% 100.000%

Gather stats latency (us):
Count: 46 Average: 980387.5870  StdDev: 60911.18
Min: 879155  Median: 1033777.7778  Max: 1261431
Percentiles: P50: 1033777.78 P75: 1120666.67 P99: 1261431.00 P99.9: 1261431.00 P99.99: 1261431.00
------------------------------------------------------
(  860000, 1200000 ]       45  97.826%  97.826% ####################
( 1200000, 1900000 ]        1   2.174% 100.000%

Most recent cache entry stats:
Number of entries: 1295133
Total charge: 9.88 GB
Average key size: 23.4982
Average charge: 8.00 KB
Unique deleters: 3
```

Reviewed By: mrambacher

Differential Revision: D28295742

Pulled By: pdillinger

fbshipit-source-id: bbc4a552f91ba0fe10e5cc025c42cef5a81f2b95