I have insert to s3 6bil rows table:
```
INSERT INTO FUNCTION
s3('https://s3.us-east-1.amazonaws.com/my-bucket/data.csv', 'DFGHJ', 'FGHJIJH, 'CSVWithNames', 'time DateTime, exchangeId UInt16, pairId UInt16, id String, price Decimal(38, 18), volume Decimal(38, 18)')
SELECT * FROM trade;
```
but it fails trying to upload part 10001 while aws s3 upload [supports](https://docs.aws.amazon.com/AmazonS3/latest/userguide/qfacts.html) only 10k parts.

I had to export this select into CSV 600GB file on the disk and then perform successful `aws s3 cp data.csv s3://my-bucket/data.csv`

I expected Clickhouse would do it on his own successfully. 

┌─version()─┐
│ 22.1.3.7  │
└───────────┘

M(UInt64, min_count_to_compile_aggregate_expression, 3, "The number of identical aggregate expressions before they are JIT-compiled", 0) \
    M(UInt64, group_by_two_level_threshold, 100000, "From what number of keys, a two-level aggregation starts. 0 - the threshold is not set.", 0) \
    M(UInt64, group_by_two_level_threshold_bytes, 50000000, "From what size of the aggregation state in bytes, a two-level aggregation begins to be used. 0 - the threshold is not set. Two-level aggregation is used when at least one of the thresholds is triggered.", 0) \
    M(Bool, distributed_aggregation_memory_efficient, true, "Is the memory-saving mode of distributed aggregation enabled.", 0) \
    M(UInt64, aggregation_memory_efficient_merge_threads, 0, "Number of threads to use for merge intermediate aggregation results in memory efficient mode. When bigger, then more memory is consumed. 0 means - same as 'max_threads'.", 0) \
    M(Bool, enable_positional_arguments, false, "Enable positional arguments in ORDER BY, GROUP BY and LIMIT BY", 0) \
    \
    M(UInt64, max_parallel_replicas, 1, "The maximum number of replicas of each shard used when the query is executed. For consistency (to get different parts of the same partition), this option only works for the specified sampling key. The lag of the replicas is not controlled.", 0) \
    M(UInt64, parallel_replicas_count, 0, "", 0) \
    M(UInt64, parallel_replica_offset, 0, "", 0) \
    \
    M(Bool, allow_experimental_parallel_reading_from_replicas, false, "If true, ClickHouse will send a SELECT query to all replicas of a table. It will work for any kind on MergeTree table.", 0) \
    \
    M(Bool, skip_unavailable_shards, false, "If true, ClickHouse silently skips unavailable shards and nodes unresolvable through DNS. Shard is marked as unavailable when none of the replicas can be reached.", 0) \
    \
    M(UInt64, parallel_distributed_insert_select, 0, "Process distributed INSERT SELECT query in the same cluster on local tables on every shard, if 1 SELECT is executed on each shard, if 2 SELECT and INSERT is executed on each shard", 0) \
    M(UInt64, distributed_group_by_no_merge, 0, "If 1, Do not merge aggregation states from different servers for distributed queries (shards will process query up to the Complete stage, initiator just proxies the data from the shards). If 2 the initiator will apply ORDER BY and LIMIT stages (it is not in case when shard process query up to the Complete stage)", 0) \
    M(UInt64, distributed_push_down_limit, 1, "If 1, LIMIT will be applied on each shard separatelly. Usually you don't need to use it, since this will be done automatically if it is possible, i.e. for simple query SELECT FROM LIMIT.", 0) \
    M(Bool, optimize_distributed_group_by_sharding_key, true, "Optimize GROUP BY sharding_key queries (by avoiding costly aggregation on the initiator server).", 0) \
    M(UInt64, optimize_skip_unused_shards_limit, 1000, "Limit for number of sharding key values, turns off optimize_skip_unused_shards if the limit is reached", 0) \
    M(Bool, optimize_skip_unused_shards, false, "Assumes that data is distributed by sharding_key. Optimization to skip unused shards if SELECT query filters by sharding_key.", 0) \
    M(Bool, optimize_skip_unused_shards_rewrite_in, true, "Rewrite IN in query for remote shards to exclude values that does not belong to the shard (requires optimize_skip_unused_shards)", 0) \
    M(Bool, allow_nondeterministic_optimize_skip_unused_shards, false, "Allow non-deterministic functions (includes dictGet) in sharding_key for optimize_skip_unused_shards", 0) \
    M(UInt64, force_optimize_skip_unused_shards, 0, "Throw an exception if unused shards cannot be skipped (1 - throw only if the table has the sharding key, 2 - always throw.", 0) \
    M(UInt64, optimize_skip_unused_shards_nesting, 0, "Same as optimize_skip_unused_shards, but accept nesting level until which it will work.", 0) \
    M(UInt64, force_optimize_skip_unused_shards_nesting, 0, "Same as force_optimize_skip_unused_shards, but accept nesting level until which it will work.", 0) \
    \
    M(Bool, input_format_parallel_parsing, true, "Enable parallel parsing for some data formats.", 0) \
    M(UInt64, min_chunk_bytes_for_parallel_parsing, (10 * 1024 * 1024), "The minimum chunk size in bytes, which each thread will parse in parallel.", 0) \
    M(Bool, output_format_parallel_formatting, true, "Enable parallel formatting for some data formats.", 0) \