Understanding gaps detected by cartographer_rosbag_validate

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I'm using cartographer for SLAM, and because I'm not getting good results (map rotates), I'm validating my bags. Validate detected in particular that I have large gaps in /scan sensor data (it also detected some deltas between sensor and serialization data, I don't know if that's relevant to the gaps problem). What does this mean? Is it referring to a time gap in between two /scan messages?

I'm using YDLIDAR X4, which states it operates at 7 hz. Does this mean it will produce aprox one /scan msg every 142 ms? If that's so, I don't see how can I achieve a (much) lower gap as required by cartographer. But given I've seen other people achieve good results with YDLIDAR X4 and cartographer, maybe I'm misunderstanding something.

What strategies are recommended to reduce msg gaps in sensor data like /scan? Below is my validate text output:

W0110 15:29:44.522763 19183 rosbag_validate_main.cc:352] frame_id "laser_frame" on topic /scan has serialization time 1578680789.040276050 but sensor time 1578680788.747516000 differing by -0.29276 s.
E0110 15:29:44.525462 19183 rosbag_validate_main.cc:350] frame_id "laser_frame" on topic /scan has serialization time 1578680797.428982973 but sensor time 1578680796.891624000 differing by -0.537359 s.
E0110 15:29:44.525600 19183 rosbag_validate_main.cc:350] frame_id "laser_frame" on topic /scan has serialization time 1578680797.570624113 but sensor time 1578680797.007191000 differing by -0.563433 s.
E0110 15:29:44.525665 19183 rosbag_validate_main.cc:350] frame_id "laser_frame" on topic /scan has serialization time 1578680797.713319063 but sensor time 1578680797.120317000 differing by -0.593002 s.
E0110 15:29:44.537004 19183 rosbag_validate_main.cc:382] Point data (frame_id: "laser_frame" has a large gap, largest is 0.125339 s, recommended is [0.0005, 0.05] s with no jitter.
I0110 15:29:44.537109 19183 rosbag_validate_main.cc:398] Time delta histogram for consecutive messages on topic "/scan" (frame_id: "laser_frame":
Count: 392  Min: 0.102170  Max: 0.125339  Mean: 0.114846
[0.102170, 0.104487)                        Count: 2 (0.510204%) Total: 2 (0.510204%)
[0.104487, 0.106804)                        Count: 7 (1.785714%) Total: 9 (2.295918%)
[0.106804, 0.109121)                     ## Count: 31 (7.908163%)   Total: 40 (10.204082%)
[0.109121, 0.111438)                        Count: 0 (0.000000%) Total: 40 (10.204082%)
[0.111438, 0.113755)                      # Count: 26 (6.632653%)   Total: 66 (16.836735%)
[0.113755, 0.116071)         ############## Count: 273 (69.642860%) Total: 339 (86.479591%)
[0.116071, 0.118388)                      # Count: 13 (3.316327%)   Total: 352 (89.795921%)
[0.118388, 0.120705)                        Count: 2 (0.510204%) Total: 354 (90.306122%)
[0.120705, 0.123022)                      # Count: 26 (6.632653%)   Total: 380 (96.938774%)
[0.123022, 0.125339]                      # Count: 12 (3.061224%)   Total: 392 (100.000000%)

Thanks!