Fix some formatting of compaction blog post

docs/_posts/2016-01-29-compaction_pri.markdown

@@ -21,26 +21,26 @@ When we estimate write amplification, we usually simplify the problem by assumin
 
 If all the updates are uniformly distributed, LevelDB's approach optimizes write amplification, because a file being picked covers a range whose last compaction time to the next level is the oldest, so the range will accumulated keys from incoming compactions for the longest and the density is the highest.
 
-We created a compaction priority **kOldestSmallestSeqFirst **for the same effect. With this mode, we always pick the file covers the oldest updates in the level, which usually is contains the densest key range. If you have a use case where writes are uniformly distributed across the key space and you want to reduce write amplification, you should set options.compaction_pri=kOldestSmallestSeqFirst.
+We created a compaction priority **kOldestSmallestSeqFirst** for the same effect. With this mode, we always pick the file covers the oldest updates in the level, which usually is contains the densest key range. If you have a use case where writes are uniformly distributed across the key space and you want to reduce write amplification, you should set options.compaction_pri=kOldestSmallestSeqFirst.
 
 **Optimize for small working set**
 
 We are assuming updates are uniformly distributed across the whole key space in previous analysis. However, in many use cases, there are subset of keys that are frequently updated while other key ranges are very cold. In this case, keeping hot key ranges from compacting to deeper levels will benefit write amplification, as well as space amplification. For example, if in a DB only key 150-160 are updated and other keys are seldom updated. If level 1 contains 20 keys, we want to keep 150-160 all stay in level 1. Because when next level 0 -> 1 compaction comes, it will simply overwrite existing keys so size level 1 doesn't increase, so no need to schedule further compaction for level 1->2. On the other hand, if we compact key 150-155 to level2, when a new Level 1->2 compaction comes, it increases the size of level 1, making size of level 1 exceed target size and more compactions will be needed, which generates more writes.
 
-The compaction priority **kOldestLargestSeqFirst **optimizes this use case. In this mode, we will pick a file whose latest update is the oldest. It means there is no incoming data for the range for the longest. Usually it is the coldest range. By compacting coldest range first, we leave the hot ranges in the level. If your use case is to overwrite existing keys in a small range, try options.compaction_pri=kOldestLargestSeqFirst**.**
+The compaction priority **kOldestLargestSeqFirst** optimizes this use case. In this mode, we will pick a file whose latest update is the oldest. It means there is no incoming data for the range for the longest. Usually it is the coldest range. By compacting coldest range first, we leave the hot ranges in the level. If your use case is to overwrite existing keys in a small range, try options.compaction_pri=kOldestLargestSeqFirst**.**
 
 **Drop delete marker sooner**
 
 If one file contains a lot of delete markers, it may slow down iterating over this area, because we still need to iterate those deleted keys just to ignore them. Furthermore, the sooner we compact delete keys into the last level, the sooner the disk space is reclaimed, so it is good for space efficiency.
 
-Our default compaction priority **kByCompensatedSize **considers the case. If number of deletes in a file exceeds number of inserts, it is more likely to be picked for compaction. The more number of deletes exceed inserts, the more likely it is being compacted. The optimization is added to avoid the worst performance of space efficiency and query performance when a large percentage of the DB is deleted.
+Our default compaction priority **kByCompensatedSize** considers the case. If number of deletes in a file exceeds number of inserts, it is more likely to be picked for compaction. The more number of deletes exceed inserts, the more likely it is being compacted. The optimization is added to avoid the worst performance of space efficiency and query performance when a large percentage of the DB is deleted.
 
 **Efficiency of compaction filter**
 
 Usually people use [compaction filters](https://github.com/facebook/rocksdb/blob/v4.1/include/rocksdb/options.h#L201-L226) to clean up old data to free up space. Picking files to compact may impact space efficiency. We don't yet have a a compaction priority to optimize this case. In some of our use cases, we solved the problem in a different way: we have an external service checking modify time of all SST files. If any of the files is too old, we force the single file to compaction by calling DB::CompactFiles() using the single file. In this way, we can provide a time bound of data passing through compaction filters.
 
 
-In all, there three choices of compaction priority modes optimizing different scenarios. if you have a new use case, we suggest you start with options.compaction_pri=kOldestSmallestSeqFirst (note it is not the default one for backward compatible reason). If you want to further optimize your use case, you can try other two use cases if your use cases apply.
+In all, there three choices of compaction priority modes optimizing different scenarios. if you have a new use case, we suggest you start with `options.compaction_pri=kOldestSmallestSeqFirst` (note it is not the default one for backward compatible reason). If you want to further optimize your use case, you can try other two use cases if your use cases apply.
 
 If you have good ideas about better compaction picker approach, you are welcome to implement and benchmark it. We'll be glad to review and merge your a pull requests.