CORE-207: TSV download optimizations in makeRow()

[davidangb]

This PR is an optimization found while researching CORE-207. This should be performance only; it has no functional impact to end users.

The makeRow() function is called, as its name implies, for each row of a TSV during download. Thus, it feels worth optimizing.

1. optimize use of collections within TSVFormatter.makeRow. Previously, we looped over all attributes in the entity, performed a Seq.indexOf() call to determine the column number that attribute should reside in, made a map of the column number->value, and then created a Seq of values based on the map's keys. Now, we loop over the headers in order and extract each column from the entity, still in order. This is less work and makes the makeRow function ~1.5x faster.
2. use List instead of IndexedSeq when iterating. We were using IndexedSeq all over the place. IndexedSeq is optimized more for random access, while List (which extends LinearSeq) is optimized more for sequential access. Our access was sequential. Changing this added another ~1.4x speedup, for a total of ~2.2x.

Raw numbers from the JMH benchmark:

	ops/sec	error +/-	vs.baseline	avg ops/sec	avg vs. baseline
baseline	244,195.39	19,087.54		247,734.34
	240,787.36	21,066.81	98.60%
	258,220.27	14,516.10	105.74%
Map->Seq for makerow	388,638.98	48,430.52	159.15%	398,863.48	161.00%
	375,149.40	43,361.68	153.63%
	325,224.09	45,584.79	133.18%
	315,577.37	51,806.07	129.23%
	410,244.36	38,258.30	168.00%
IndexedSeq -> List	578,346.66	8,294.20	236.84%	555,535.68	224.25%
	559,476.43	72,216.37	229.11%
	546,860.46	24,416.72	223.94%
	537,459.14	25,614.79	220.09%

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• I've followed the instructions if I've made any changes to the API, especially if they're breaking changes
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[davidangb]

I deleted the two example benchmarks and replaced with a benchmark that is actually useful for the current case.