Benchmark table reader wiht nanoseconds

Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results.

Test Plan:
sample output:

    ./table_reader_bench --plain_table --time_unit=nanosecond
    =======================================================================================================
    InMemoryTableSimpleBenchmark:           PlainTable   num_key1:   4096   num_key2:   512   non_empty
    =======================================================================================================
    Histogram (unit: nanosecond):
    Count: 6291456  Average: 475.3867  StdDev: 556.05
    Min: 135.0000  Median: 400.1817  Max: 33370.0000
    Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21
    ------------------------------------------------------
    [     120,     140 )        2   0.000%   0.000%
    [     140,     160 )      452   0.007%   0.007%
    [     160,     180 )    13683   0.217%   0.225%
    [     180,     200 )    54353   0.864%   1.089%
    [     200,     250 )   101004   1.605%   2.694%
    [     250,     300 )   729791  11.600%  14.294% ##
    [     300,     350 )   616070   9.792%  24.086% ##
    [     350,     400 )  1628021  25.877%  49.963% #####
    [     400,     450 )   647220  10.287%  60.250% ##
    [     450,     500 )   577206   9.174%  69.424% ##
    [     500,     600 )  1168585  18.574%  87.999% ####
    [     600,     700 )   506875   8.057%  96.055% ##
    [     700,     800 )   147878   2.350%  98.406%
    [     800,     900 )    42633   0.678%  99.083%
    [     900,    1000 )    16304   0.259%  99.342%
    [    1000,    1200 )     7811   0.124%  99.466%
    [    1200,    1400 )     1453   0.023%  99.490%
    [    1400,    1600 )      307   0.005%  99.494%
    [    1600,    1800 )       81   0.001%  99.496%
    [    1800,    2000 )       18   0.000%  99.496%
    [    2000,    2500 )        8   0.000%  99.496%
    [    2500,    3000 )        6   0.000%  99.496%
    [    3500,    4000 )        3   0.000%  99.496%
    [    4000,    4500 )      116   0.002%  99.498%
    [    4500,    5000 )     1144   0.018%  99.516%
    [    5000,    6000 )     1087   0.017%  99.534%
    [    6000,    7000 )     2403   0.038%  99.572%
    [    7000,    8000 )     9840   0.156%  99.728%
    [    8000,    9000 )    12820   0.204%  99.932%
    [    9000,   10000 )     3881   0.062%  99.994%
    [   10000,   12000 )      135   0.002%  99.996%
    [   12000,   14000 )      159   0.003%  99.998%
    [   14000,   16000 )       58   0.001%  99.999%
    [   16000,   18000 )       30   0.000% 100.000%
    [   18000,   20000 )       14   0.000% 100.000%
    [   20000,   25000 )        2   0.000% 100.000%
    [   25000,   30000 )        2   0.000% 100.000%
    [   30000,   35000 )        1   0.000% 100.000%

Reviewers: haobo, dhruba, sdong

CC: leveldb

Differential Revision: https://reviews.facebook.net/D16113