We present MetaPartMin and MetaPart, two new lightweight parallel metagenomic read partitioning strategies. Metagenomic data partitioning can aid the concurrent de novo assembly of partitions. Prior read partitioning methods tend to create a giant component of reads. We avoid this problem with new heuristics amenable to statically load-balanced parallelization. Our strategies require enumerating and sorting k-mers and minimizers from the input read sequences, and traversing an implicit graph to identify components. MetaPartMin uses minimizers to significantly lower aggregate main memory use, thereby enabling the processing of massive datasets on a modest number of compute nodes. All steps in our strategies exploit hybrid multicore and distributed-memory parallelism. We demonstrate scaling and efficiency on a collection of large-scale datasets. MetaPartMin can process a 1.25 terabase soil metagenome in 6 minutes on just 32 Intel Skylake nodes (48 cores each) of the Stampede2 supercomputer, and a 252 gigabase soil metagenome in 54 seconds on 16 Stampede2 Skylake nodes. The source code is available at https://github.com/vasupsu/MetaPart.