```sql
CREATE TABLE test_tbl
(   `ip` String, `ipv6` IPv6 MATERIALIZED toIPv6(ip) )
ENGINE = Memory;

insert into test_tbl(ip) values ( 'fe80::9801:43ff:fe1f:7690'), ('1.1.1.1'), (''), ('::ffff:1.1.1.1' );

 DB::Exception: Invalid IPv6 value: while executing 'FUNCTION _CAST(toIPv6(ip) :: 2, 'IPv6' :: 1)


set cast_ipv4_ipv6_default_on_conversion_error=1;
insert into test_tbl(ip) values ( 'fe80::9801:43ff:fe1f:7690'), ('1.1.1.1'), (''), ('::ffff:1.1.1.1' );
 
 DB::Exception: Invalid IPv6 value: while executing 'FUNCTION _CAST(toIPv6(ip)
```

`toIPv6OrDefault` does not help in this case.

```sql
CREATE TABLE test_tbl
(   `ip` String, `ipv6` IPv6 MATERIALIZED toIPv6OrDefault(ip) )
ENGINE = Memory;

insert into test_tbl(ip) values ( 'fe80::9801:43ff:fe1f:7690'), ('1.1.1.1'), (''), ('::ffff:1.1.1.1' );
  DB::Exception: Invalid IPv6 value: while executing 'FUNCTION _CAST(toIPv6OrD

insert into test_tbl(ip) values ( 'fe80::9801:43ff:fe1f:7690'), ('1.1.1.1'), (''), ('::ffff:1.1.1.1' ), ('garbudge');
  DB::Exception: Invalid IPv6 value: while executing 'FUNCTION
```

WA:

```sql
CREATE TABLE test_tbl
(   `ip` String, `ipv6` IPv6 MATERIALIZED if(ip='', '::', ip) )
ENGINE = Memory;

insert into test_tbl(ip) values ( 'fe80::9801:43ff:fe1f:7690'), ('1.1.1.1'), (''), ('::ffff:1.1.1.1' );

select ip, ipv6 from test_tbl;
┌─ip────────────────────────┬─ipv6──────────────────────┐
│ fe80::9801:43ff:fe1f:7690 │ fe80::9801:43ff:fe1f:7690 │
│ 1.1.1.1                   │ ::ffff:1.1.1.1            │
│                           │ ::                        │
│ ::ffff:1.1.1.1            │ ::ffff:1.1.1.1            │
└───────────────────────────┴───────────────────────────┘
```
M(LoadBalancing, load_balancing, LoadBalancing::RANDOM, "Which replicas (among healthy replicas) to preferably send a query to (on the first attempt) for distributed processing.", 0) \
    M(UInt64, load_balancing_first_offset, 0, "Which replica to preferably send a query when FIRST_OR_RANDOM load balancing strategy is used.", 0) \
    \
    M(TotalsMode, totals_mode, TotalsMode::AFTER_HAVING_EXCLUSIVE, "How to calculate TOTALS when HAVING is present, as well as when max_rows_to_group_by and group_by_overflow_mode = ‘any’ are present.", IMPORTANT) \
    M(Float, totals_auto_threshold, 0.5, "The threshold for totals_mode = 'auto'.", 0) \
    \
    M(Bool, allow_suspicious_low_cardinality_types, false, "In CREATE TABLE statement allows specifying LowCardinality modifier for types of small fixed size (8 or less). Enabling this may increase merge times and memory consumption.", 0) \
    M(Bool, compile_expressions, true, "Compile some scalar functions and operators to native code.", 0) \
    M(UInt64, min_count_to_compile_expression, 3, "The number of identical expressions before they are JIT-compiled", 0) \
    M(Bool, compile_aggregate_expressions, true, "Compile aggregate functions to native code.", 0) \
    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 set to 1 - SELECT is executed on each shard; if set to 2 - SELECT and INSERT are 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) \