Update sparse-primary-indexes.md

Robert Schulze's 4 edits have been added.

docs/en/guides/best-practices/sparse-primary-indexes.md

@@ -141,7 +141,7 @@ To make this (way) more efficient and (much) faster, we need to use a table with
 
 ### An index design for massive data scales
 
-In traditional relational database management systems, the primary index would contain one entry per table row. This would result in the primary index containing 8.87 million entries for our data set. Such an index allows the fast location of specific rows, resulting in high efficiency for lookup queries and point updates. Searching an entry in a `B(+)-Tree` data structure has an average time complexity of `O(log2 n)`; more precisely, `log_b n = log2 n / log2 b` where `b` is the branching factor of the `B(+)-Tree`. Because `b` is typically between several hundred and several thousand, `B(+)-Trees` are very shallow structures, and few disk-seeks are required to locate records. With 8.87 million rows and a branching factor of 1000, three disk-seeks are needed. This capability comes at a cost: additional disk and memory overheads, higher insertion costs when adding new rows to the table and entries to the index, and sometimes rebalancing of the B-Tree).
+In traditional relational database management systems, the primary index would contain one entry per table row. This would result in the primary index containing 8.87 million entries for our data set. Such an index allows the fast location of specific rows, resulting in high efficiency for lookup queries and point updates. Searching an entry in a `B(+)-Tree` data structure has an average time complexity of `O(log n)`; more precisely, `log_b n = log_2 n / log_2 b` where `b` is the branching factor of the `B(+)-Tree` and `n` is the number of indexed rows. Because `b` is typically between several hundred and several thousand, `B(+)-Trees` are very shallow structures, and few disk-seeks are required to locate records. With 8.87 million rows and a branching factor of 1000, 2.3 disk seeks are needed on average. This capability comes at a cost: additional disk and memory overheads, higher insertion costs when adding new rows to the table and entries to the index, and sometimes rebalancing of the B-Tree.
 
 Considering the challenges associated with B-Tree indexes, table engines in ClickHouse utilise a different approach. The ClickHouse [MergeTree Engine Family](/docs/en/engines/table-engines/mergetree-family/index.md) has been designed and optimized to handle massive data volumes. These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. Data is quickly written to a table [part by part](/docs/en/engines/table-engines/mergetree-family/mergetree.md/#mergetree-data-storage), with rules applied for merging the parts in the background. In ClickHouse each part has its own primary index. When parts are merged, then the merged part’s primary indexes are also merged. At the very large scale that ClickHouse is designed for, it is paramount to be very disk and memory efficient. Therefore, instead of indexing every row, the primary index for a part has one index entry (known as a ‘mark’) per group of rows (called ‘granule’) - this technique is called **sparse index**.