**Summary**
After upgrading a host that is just a collection `Distributed` tables to 20.4.X and 20.5.2.7, we observe the new `MemoryTracking` setting increasing substantially during writes, leading to individual writes and reads yielding exceptions: ```Memory limit (total) exceeded: would use 28.28 GiB (attempt to allocate chunk of 4197840 bytes), maximum: 28.28```

System metrics show that no actual memory is being allocated to the same size.
<img width="993" alt="Screen Shot 2020-07-19 at 12 23 59 PM" src="https://user-images.githubusercontent.com/1189446/87883721-229e7280-c9be-11ea-851b-0e1511cc10bb.png">

Please let me know if there is any additional context, diagrams, or statements I should provide. I'm interested in knowing how to correlate the total `MemoryTracking` metric to anything else since system memory does not reflect the metric at all.

**Context**
This host has four different `Distributed` tables that are written to via the HTTP interface with `insert_distributed_sync=1` set.
The host itself has no local tables.

The hosts with the downstream local tables are all 20.5.2.7.

We have downgraded the `Distributed` host in question back to 20.3.9.70 in order to continue writing to the clusters.
#include <DataStreams/ParallelParsingBlockInputStream.h>
namespace DB
{
void ParallelParsingBlockInputStream::segmentatorThreadFunction()
{
    setThreadName("Segmentator");
    try
    {
        while (!finished)
        {
            const auto current_unit_number = segmentator_ticket_number % processing_units.size();
            auto & unit = processing_units[current_unit_number];
            {
                std::unique_lock<std::mutex> lock(mutex);
                segmentator_condvar.wait(lock,
                    [&]{ return unit.status == READY_TO_INSERT || finished; });
            }
            if (finished)
            {
                break;
            }