Snowflake/Arctic-Text2SQL-R1-7B Fine-tuned for NL2SQL++ v8

This model is a fine-tuned version of Snowflake/Arctic-Text2SQL-R1-7B on the NL2SQL++ v8 dataset with code-with-thought reasoning.

Model Details

Base Model: Snowflake/Arctic-Text2SQL-R1-7B
Task: Text-to-SQL generation
Dataset: NL2SQL++ v8 with code-with-thought reasoning
Fine-tuning Method: LoRA (Low-Rank Adaptation) with Unsloth
Quantization: 16-bit merged weights
Training Dataset Size: (20886, 1) examples
Validation Dataset Size: (1066, 1) examples

Training Configuration

output_dir: trainer_output
overwrite_output_dir: False
do_train: False
do_eval: True
do_predict: False
eval_strategy: IntervalStrategy.STEPS
prediction_loss_only: False
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: 10
eval_delay: 0
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: -1
lr_scheduler_type: SchedulerType.COSINE
lr_scheduler_kwargs: None
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_dir: trainer_output/runs/Apr02_09-38-14_ip-172-31-32-148.ap-southeast-2.compute.internal
logging_strategy: IntervalStrategy.STEPS
logging_first_step: False
logging_steps: 0.004
logging_nan_inf_filter: True
save_strategy: SaveStrategy.BEST
save_steps: 0.04
save_total_limit: 2
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 3407
data_seed: None
jit_mode_eval: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
eval_steps: 0.04
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
run_name: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
metric_for_best_model: eval_loss
greater_is_better: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False)
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: OptimizerNames.ADAMW_TORCH_FUSED
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
report_to: ['wandb']
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: None
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: HubStrategy.EVERY_SAVE
hub_token: None
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: True
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
push_to_hub_token: None
_n_gpu: 1
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
model_init_kwargs: None
chat_template_path: None
dataset_text_field: text
dataset_kwargs: None
dataset_num_proc: None
eos_token: None
pad_token: None
max_length: 1024
packing: False
packing_strategy: bfd
padding_free: False
pad_to_multiple_of: None
eval_packing: None
completion_only_loss: None
assistant_only_loss: False
loss_type: nll
activation_offloading: False
vllm_sampling_params: None
unsloth_num_chunks: -1
unsloth_logit_chunk_multiplier: None
unsloth_grpo_mini_batch: None
max_seq_length: 17000
model_name: Snowflake/Arctic-Text2SQL-R1-7B
train_batch_size: 8
val_batch_size: 1
num_epochs: 3
lora_r: 64
lora_alpha: 128

Train Dataset Example

<|im_start|>system
You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>
<|im_start|>user

You are an expert in SQL++ query generation. Given a document schema and a natural language query, generate a valid SQL++ query.

CORE RULES:
1. Column names with spaces or special characters (e.g., "Enrollment (K-12)") must be enclosed in backticks.
2. SUBSTR uses 0-based indexing: SUBSTR(str, 0, 4) gets the first 4 characters.
3. Prioritize evidence over natural language if they conflict.
4. Only use provided keyspaces and existing fields.
5. Never use SELECT *; be explicit about columns.
6. Never use CAST() function because it's not supported in SQL++.

SELECT REQUIREMENTS:
- Main SELECT must NOT use RAW; return only standard expressions.
- RAW is allowed only in SELECT subqueries.
- Scalar subqueries must use IN (SELECT RAW ...) or = (SELECT RAW ...)[0].
- Always append [0] to SELECT RAW when used as a scalar.
- NEVER use [0] with aggregate functions (MAX, MIN, AVG, etc.).

JOIN & QUERY STRUCTURE:
- Use JOINs only when required; never use correlated subqueries.
- Never self-JOIN unless explicitly needed.
- Don't repeat table aliases; don't name aliases after buckets/tables.
- No GROUP BY for single keyspace queries.

FIELD & NULL HANDLING:
- Every field in final SELECT or WHERE must have IS NOT NULL in WHERE clause unless explicitly checking for NULL.
- If an object is requested without specific attributes, return its id.

AGGREGATION & CALCULATIONS:
- Forbidden functions: DIVIDE(), SUBTRACT(), YEAR(), ARRAY_AGG().
- Use DISTINCT when unique entities are requested.
- PERCENTAGE RULE: numerator and denominator must come from the SAME row set. Never compute denominator in a separate subquery. Define one base set (FROM/JOIN/WHERE), compute denominator from it, and derive numerator by adding conditions in aggregate expressions (CASE WHEN).

FORMATTING:
- No colon after FROM.
- Return ONLY what is asked.

Bucket Name: training_bucket
Scope Name: Advertising_Attribution_and_ROI_Analytics_scope

Document Schema:
{"`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`attribution_results`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_149fbd20-8708-4124-..."], "type": "string"}, "attribution_model_id": {"samples": ["attr_model_6ca6427a-0123-4a31-..."], "type": "string"}, "computed_at": {"samples": ["2025-10-25T00:18:54Z"], "type": "string"}, "conversion_id": {"samples": ["conversion_3d0ec24c-bc6a-4bac-..."], "type": "string"}, "created_at": {"samples": ["2025-10-25T00:18:59Z"], "type": "string"}, "credits": {"items": {}, "samples": [[{"ad_group_id": null, "campaign_id": null, "channel_id": "channel_fd1d3391-152f-4e92-b9cf-b3cc0afe1362", "creative_id": null, "credited_conversions": 1, "credited_revenue": 11.63, "currency": "USD", "occurred_at": "2025-10-25T16:59:35Z", "touchpoint_id": "touchpoint_912bd90c-ecc2-4989-82d7-615889ddb438", "touchpoint_type": "ad_click", "weight": 1}]], "type": "array"}, "explain": {"properties": {"dropped_touchpoints": {"items": {}, "samples": [[{"reason": "outside_rules", "touchpoint_id": "touchpoint_4a40ba11-66b6-46c6-af40-c66794c952a9"}]], "type": "array"}, "notes": {"samples": [["Model applied with lookback an..."]], "type": "string"}}, "type": "object"}, "person_id": {"samples": [["person_311faa53-8c2c-4fe6-bd2e..."]], "type": "string"}, "totals": {"properties": {"currency": {"samples": [["AUD"]], "type": "string"}, "total_credited_conversions": {"samples": [[1]], "type": "number"}, "total_credited_revenue": {"samples": [[11.63]], "type": "number"}}, "type": "object"}, "window": {"properties": {"from": {"samples": ["2025-09-10T00:17:54Z"], "type": "string"}, "to": {"samples": ["2025-10-25T00:17:54Z"], "type": "string"}}, "type": "object"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`attribution_models`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_40b38653-2ba0-4f82-..."], "type": "string"}, "created_at": {"samples": ["2025-03-03T19:53:28Z"], "type": "string"}, "is_default": {"samples": [false], "type": "boolean"}, "lookback_windows": {"properties": {"click_days": {"samples": [7], "type": "number"}, "view_days": {"samples": [1], "type": "number"}}, "type": "object"}, "model_type": {"samples": ["first_click"], "type": "string"}, "name": {"samples": ["First Click v1"], "type": "string"}, "rules": {"properties": {"dedupe_within_minutes": {"samples": [[10]], "type": "number"}, "exclude_channels": {"items": {"type": "string"}, "samples": [["affiliate"]], "type": "array"}, "include_channels": {"items": {"type": "string"}, "samples": [["affiliate"]], "type": "array"}}, "type": "object"}, "updated_at": {"samples": [["2025-02-16T17:35:41Z"]], "type": "string"}, "version": {"samples": [1], "type": "number"}, "weights": {"items": {"properties": {"touchpoint_type": {"samples": ["ad_click"], "type": "string"}, "weight": {"samples": [1], "type": "number"}}, "type": "object"}, "samples": [[{"touchpoint_type": "ad_click", "weight": 0.5}]], "type": "array"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`costs`": {"Flavor": "", "properties": {"ad_group_id": {"samples": [["ad_group_1d81992b-7ae7-45bc-bb..."]], "type": "string"}, "advertiser_id": {"samples": ["advertiser_2f6e754e-c2f0-4ae5-..."], "type": "string"}, "campaign_id": {"samples": [["campaign_794729c9-e191-4ca1-83..."]], "type": "string"}, "channel_id": {"samples": ["channel_17aea452-560e-403a-a2b..."], "type": "string"}, "clicks": {"samples": [[198]], "type": "number"}, "creative_id": {"samples": [["creative_2c8d15d9-6146-430d-a0..."]], "type": "string"}, "currency": {"samples": ["AUD"], "type": "string"}, "date": {"samples": ["2025-11-22"], "type": "string"}, "fees": {"properties": {"agency_fees": {"samples": [[25.24]], "type": "number"}, "platform_fees": {"samples": [[0.34]], "type": "number"}}, "type": "object"}, "granularity": {"samples": ["daily"], "type": "string"}, "impressions": {"samples": [[4085]], "type": "number"}, "ingested_at": {"samples": ["2025-11-22T20:05:14Z"], "type": "string"}, "source": {"samples": ["api"], "type": "string"}, "spend": {"samples": [622.44], "type": "number"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`conversions`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_0060c7cf-3cec-4368-..."], "type": "string"}, "conversion_type": {"samples": ["lead"], "type": "string"}, "created_at": {"samples": ["2025-10-29T15:48:17Z"], "type": "string"}, "dedupe_key": {"samples": [["dd_02c3ee0b2ce74aa9a3"]], "type": "string"}, "event_source": {"samples": ["offline_import"], "type": "string"}, "external_event_id": {"samples": [["ext_034fdd40765440b98b66ac5435..."]], "type": "string"}, "occurred_at": {"samples": ["2025-10-29T14:04:17Z"], "type": "string"}, "order": {"properties": {"currency": {"samples": ["AUD"], "type": "string"}, "discount": {"samples": [[16.41]], "type": "number"}, "items": {"items": {}, "samples": [[{"name": "Down", "product_id": null, "quantity": 1, "revenue": 236.31, "sku": "SKU-68405", "unit_price": 236.31}]], "type": "array"}, "order_id": {"samples": [["ord_0ad1403de4df"]], "type": "string"}, "shipping": {"samples": [[0]], "type": "number"}, "subtotal": {"samples": [[441.25]], "type": "number"}, "tax": {"samples": [[20.04]], "type": "number"}, "total": {"samples": [1.92], "type": "number"}}, "type": "object"}, "person_id": {"samples": [["person_2b90965e-911c-44ee-9ebd..."]], "type": "string"}, "updated_at": {"samples": [["2025-11-08T15:48:17Z"]], "type": "string"}, "value": {"properties": {"currency": {"samples": [["AUD"]], "type": "string"}, "lifetime_value": {"samples": [[496.03]], "type": "number"}, "margin": {"samples": [[117.16]], "type": "number"}, "revenue": {"samples": [[3.85]], "type": "number"}}, "type": "object"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`touchpoints`": {"Flavor": "", "properties": {"ad_group_id": {"samples": [["ad_group_44136a98-ce0c-44f2-b5..."]], "type": "string"}, "advertiser_id": {"samples": ["advertiser_0053becd-41b4-4242-..."], "type": "string"}, "attribution_context": {"properties": {"page_url": {"samples": [["http://leon-roman.com/"]], "type": "string"}, "utm": {"properties": {"campaign": {"samples": [["performance"]], "type": "string"}, "content": {"samples": [["cta_b"]], "type": "string"}, "medium": {"samples": [["affiliate"]], "type": "string"}, "source": {"samples": [["facebook"]], "type": "string"}, "term": {"samples": [["plant"]], "type": "string"}}, "type": "object"}}, "type": "object"}, "campaign_id": {"samples": [["campaign_0ce273e4-c4ca-46cb-b2..."]], "type": "string"}, "channel_id": {"samples": [["channel_7194e13b-3b0a-4ab1-b58..."]], "type": "string"}, "click": {"properties": {"fbclid": {"samples": [["972c095bb74b40f0b9037643f4da19..."]], "type": "string"}, "gclid": {"samples": [["Cj0KCQ145f911493a5418c81"]], "type": "string"}, "landing_page_url": {"samples": [["http://www.dunn-rodriguez.com/"]], "type": "string"}, "msclkid": {"samples": [["1df604af63cc4dfb908a4ed85bfbf2..."]], "type": "string"}, "referrer": {"samples": [["http://bridges.biz/"]], "type": "string"}}, "type": "object"}, "created_at": {"samples": ["2025-10-07T21:29:48Z"], "type": "string"}, "creative_id": {"samples": [["creative_88551e0c-e427-4f1f-90..."]], "type": "string"}, "device": {"properties": {"device_type": {"samples": [["desktop"]], "type": "string"}, "ip": {"samples": [["163.224.62.40"]], "type": "string"}, "user_agent": {"samples": [["Opera/9.32.(Windows 95; ku-TR)..."]], "type": "string"}}, "type": "object"}, "event_id": {"samples": [["evt_1a35a2d67694406e"]], "type": "string"}, "geo": {"properties": {"city": {"samples": [["Davidburgh"]], "type": "string"}, "country": {"samples": [["AU"]], "type": "string"}, "region": {"samples": [["AB"]], "type": "string"}}, "type": "object"}, "occurred_at": {"samples": ["2025-10-07T20:41:15Z"], "type": "string"}, "person_id": {"samples": ["person_2fbaa531-d818-4730-ba1b..."], "type": "string"}, "session_id": {"samples": [["sess_d271df81fa654fc3"]], "type": "string"}, "touchpoint_type": {"samples": ["ad_click"], "type": "string"}, "tracking_link_id": {"samples": [["tracking_link_1f4bafa7-4899-4b..."]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`identities`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_a4f05d8e-b8d6-4675-..."], "type": "string"}, "consent": {"properties": {"ccpa": {"samples": [[false]], "type": "boolean"}, "consent_string": {"samples": [["20e9041e6c424ecb885d2289793ff6..."]], "type": "string"}, "consent_updated_at": {"samples": [["2025-08-02T19:39:54Z"]], "type": "string"}, "gdpr": {"samples": [[false]], "type": "boolean"}}, "type": "object"}, "created_at": {"samples": ["2025-08-24T06:09:45Z"], "type": "string"}, "expires_at": {"samples": [["2025-11-20T04:36:19Z"]], "type": "string"}, "first_seen_at": {"samples": ["2025-08-24T06:09:45Z"], "type": "string"}, "identity_type": {"samples": ["cookie"], "type": "string"}, "identity_value": {"samples": ["1192976633160"], "type": "string"}, "is_primary": {"samples": [false], "type": "boolean"}, "last_seen_at": {"samples": ["2025-09-05T03:18:46Z"], "type": "string"}, "person_id": {"samples": ["person_2daed520-58e7-41ff-ae3f..."], "type": "string"}, "source": {"samples": [["crm"]], "type": "string"}, "updated_at": {"samples": [["2025-10-06T06:09:45Z"]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`tracking_links`": {"Flavor": "", "properties": {"ad_group_id": {"samples": [["ad_group_71ddff1f-24e5-4e31-a3..."]], "type": "string"}, "advertiser_id": {"samples": ["advertiser_0c752fef-5332-40b3-..."], "type": "string"}, "campaign_id": {"samples": [["campaign_72c1fb44-646b-47c0-91..."]], "type": "string"}, "channel_id": {"samples": [["channel_9f04a2ca-45cd-4c8e-884..."]], "type": "string"}, "created_at": {"samples": ["2025-02-20T18:16:37Z"], "type": "string"}, "creative_id": {"samples": [["creative_02dd0b0a-99e2-4c2a-80..."]], "type": "string"}, "destination_url": {"samples": ["https://meyer.info/movie-stage"], "type": "string"}, "params": {"items": {"properties": {"key": {"samples": ["ref"], "type": "string"}, "value": {"samples": ["cause"], "type": "string"}}, "type": "object"}, "samples": [[{"key": "ref", "value": "above"}]], "type": "array"}, "short_url": {"samples": [["https://ex.am/3ab8a25e"]], "type": "string"}, "updated_at": {"samples": [["2025-04-16T18:16:37Z"]], "type": "string"}, "utm": {"properties": {"campaign": {"samples": [["another-wear-team"]], "type": "string"}, "content": {"samples": [["cta_b"]], "type": "string"}, "medium": {"samples": [["affiliate"]], "type": "string"}, "source": {"samples": [["bing"]], "type": "string"}, "term": {"samples": [["appear"]], "type": "string"}}, "type": "object"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`creatives`": {"Flavor": "", "properties": {"ad_group_id": {"samples": [["ad_group_72481571-e8e4-4c91-84..."]], "type": "string"}, "advertiser_id": {"samples": ["advertiser_0299d530-c60d-43c5-..."], "type": "string"}, "approval": {"properties": {"reject_reason": {"samples": [["disallowed_content"]], "type": "string"}, "review_status": {"samples": ["approved"], "type": "string"}, "reviewed_at": {"samples": [["2025-03-14T14:15:16Z"]], "type": "string"}}, "type": "object"}, "assets": {"items": {"Flavor": "`asset_type` = \"image\"", "properties": {"asset_type": {"samples": ["image"], "type": "string"}, "height": {"samples": [[90]], "type": "number"}, "text": {"samples": [], "type": "string"}, "url": {"samples": [["https://dummyimage.com/300x250"]], "type": "string"}, "width": {"samples": [[300]], "type": "number"}}, "type": "object"}, "samples": [[{"asset_type": "headline", "height": null, "text": "Various community soldier tonight more region difference.", "url": null, "width": null}]], "type": "array"}, "campaign_id": {"samples": [["campaign_0eb3a753-bdf1-493c-87..."]], "type": "string"}, "created_at": {"samples": ["2025-03-10T14:15:16Z"], "type": "string"}, "format": {"samples": ["carousel"], "type": "string"}, "landing_page": {"properties": {"final_url": {"samples": ["https://edwards-lindsey.com/hi..."], "type": "string"}, "tracking_template": {"samples": [["https://edwards-lindsey.com/hi..."]], "type": "string"}, "url": {"samples": [["https://edwards-lindsey.com/hi..."]], "type": "string"}}, "type": "object"}, "name": {"samples": ["Administration Video Creative"], "type": "string"}, "status": {"samples": ["active"], "type": "string"}, "updated_at": {"samples": [["2025-06-19T14:15:16Z"]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`ad_groups`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_7e25326b-8a75-4b67-..."], "type": "string"}, "bid": {"properties": {"amount": {"samples": [[4.89]], "type": "number"}, "currency": {"samples": [["AUD"]], "type": "string"}, "strategy": {"samples": [["cpc"]], "type": "string"}}, "type": "object"}, "campaign_id": {"samples": ["campaign_197ef716-143d-4c02-87..."], "type": "string"}, "created_at": {"samples": ["2024-12-23T07:37:40Z"], "type": "string"}, "name": {"samples": ["Activity Garden Ad Group"], "type": "string"}, "status": {"samples": ["active"], "type": "string"}, "targeting": {"properties": {"audience_segment_ids": {"items": {"type": "string"}, "samples": [["seg_001"]], "type": "array"}, "devices": {"items": {"type": "string"}, "samples": [["desktop"]], "type": "array"}, "geo": {"items": {"type": "string"}, "samples": [["CA"]], "type": "array"}}, "type": "object"}, "updated_at": {"samples": [["2025-02-01T07:37:40Z"]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`campaigns`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_0648e9cf-bbfe-4208-..."], "type": "string"}, "budget": {"properties": {"amount": {"samples": [[4149.4]], "type": "number"}, "currency": {"samples": [["AUD"]], "type": "string"}, "period": {"samples": [["daily"]], "type": "string"}}, "type": "object"}, "channel_id": {"samples": ["channel_2f163789-e8a5-4402-9c0..."], "type": "string"}, "created_at": {"samples": ["2024-12-29T14:01:06Z"], "type": "string"}, "end_date": {"samples": [["2025-07-19"]], "type": "string"}, "name": {"samples": ["Monitored optimizing extranet"], "type": "string"}, "objective": {"samples": [["awareness"]], "type": "string"}, "start_date": {"samples": [["2025-06-25"]], "type": "string"}, "status": {"samples": ["active"], "type": "string"}, "tags": {"items": {"type": "string"}, "samples": [["cost"]], "type": "array"}, "updated_at": {"samples": [["2025-02-16T13:17:50Z"]], "type": "string"}, "utm_defaults": {"properties": {"campaign": {"samples": [["always_on"]], "type": "string"}, "content": {"samples": [["carousel"]], "type": "string"}, "medium": {"samples": [["affiliate"]], "type": "string"}, "source": {"samples": [["affiliate"]], "type": "string"}, "term": {"samples": [["finish"]], "type": "string"}}, "type": "object"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`channels`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_65d17b98-5a16-4d87-..."], "type": "string"}, "created_at": {"samples": ["2024-01-24T07:11:14Z"], "type": "string"}, "name": {"samples": ["Display - dv360"], "type": "string"}, "provider": {"samples": [["dv360"]], "type": "string"}, "settings": {"properties": {"api_account_id": {"samples": [["acct_242141"]], "type": "string"}, "cost_import_mode": {"samples": [["api"]], "type": "string"}, "integration_id": {"samples": [["int_35f39b80ca"]], "type": "string"}}, "type": "object"}, "status": {"samples": ["active"], "type": "string"}, "type": {"samples": ["affiliate"], "type": "string"}, "updated_at": {"samples": [["2024-11-13T09:05:59Z"]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`users`": {"Flavor": "", "properties": {"advertiser_id": {"samples": ["advertiser_00000000-0000-0000-..."], "type": "string"}, "created_at": {"samples": ["2024-08-31T07:30:41Z"], "type": "string"}, "email": {"samples": ["bkelly@example.com"], "type": "string"}, "full_name": {"samples": ["Catherine Martin"], "type": "string"}, "last_login_at": {"samples": [["2025-10-26T11:13:23Z"]], "type": "string"}, "preferences": {"properties": {"default_attribution_model_id": {"samples": [["attr_model_00000000-0000-0000-..."]], "type": "string"}, "locale": {"samples": [["de-DE"]], "type": "string"}, "timezone": {"samples": [["America/Los_Angeles"]], "type": "string"}}, "type": "object"}, "role": {"samples": ["admin"], "type": "string"}, "status": {"samples": ["active"], "type": "string"}, "updated_at": {"samples": [["2024-11-29T07:30:41Z"]], "type": "string"}}, "type": "object"}, "`training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`advertisers`": {"Flavor": "", "properties": {"billing": {"properties": {"billing_address": {"properties": {"city": {"samples": [["Bethton"]], "type": "string"}, "country": {"samples": [["AU"]], "type": "string"}, "line1": {"samples": [["0596 Ortiz Streets Suite 612"]], "type": "string"}, "line2": {"samples": [["Apt. 254"]], "type": "string"}, "postal_code": {"samples": [["05987"]], "type": "string"}, "region": {"samples": [["KS"]], "type": "string"}}, "type": "object"}, "billing_contact_email": {"samples": [["deborahdiaz@mendez.com"]], "type": "string"}, "tax_id": {"samples": [["EU-346669959"]], "type": "string"}}, "type": "object"}, "created_at": {"samples": ["2024-04-09T08:53:26Z"], "type": "string"}, "default_currency": {"samples": ["AUD"], "type": "string"}, "industry": {"samples": [["Education"]], "type": "string"}, "legal_name": {"samples": [["Bartlett-Murphy LLC"]], "type": "string"}, "name": {"samples": ["Adams-Huff"], "type": "string"}, "status": {"samples": ["active"], "type": "string"}, "timezone": {"samples": ["America/Los_Angeles"], "type": "string"}, "updated_at": {"samples": [["2024-07-14T23:25:32Z"]], "type": "string"}}, "type": "object"}}

Natural Language Query:
For each advertiser, identify the top 5 destination URLs (from tracking links) that drove the most attributed revenue in December 2025, and show which campaigns and creatives were used to send traffic there. Add the corresponding spend from costs for those campaigns/channels in the same period, and include cases where the destination URL has attributed revenue but no direct spend records to investigate data gaps.

SQL++ Query:
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<|im_start|>assistant
<think>
1. Understand the question
- Core intent: ranking plus aggregation.
- We need, for each advertiser, the top 5 destination URLs that generated the highest attributed revenue during December 2025.
- Entities involved: advertisers, attribution results, credit rows inside attribution results, touchpoints, tracking links, campaigns, creatives, and costs.
- Temporal references:
  - attributed revenue should be based on credit occurrences in December 2025
  - spend should be based on cost records dated in December 2025
- Quantifiers:
  - “top 5” implies per-advertiser ranking
  - “for each advertiser” implies partitioned results by advertiser
- Implicit requirements:
  - destination URL comes from tracking links, not directly from attribution results
  - campaigns and creatives used to send traffic there should be collected, likely as arrays because multiple campaign/creative IDs can contribute to the same URL
  - include URLs that have revenue even if there is no spend, so spend lookup must not eliminate such rows

2. Identify relevant collections
- Primary source for attributed revenue: `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`attribution_results`
- Secondary collections:
  - `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`touchpoints` to connect credited touchpoint IDs to tracking links
  - `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`tracking_links` to get `destination_url`
  - `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`costs` to calculate spend for the same period
- Metadata access is required because the credited touchpoint ID and tracking link ID are document IDs, so joins should use `META(alias).id`

3. Map fields to collections
- From attribution results:
  - `advertiser_id`
  - `credits` array
- From each credit object in `credits`:
  - `touchpoint_id`
  - `campaign_id`
  - `creative_id`
  - `channel_id`
  - `credited_revenue`
  - `occurred_at`
- From touchpoints:
  - document ID via `META(tp).id`
  - `tracking_link_id`
- From tracking links:
  - document ID via `META(tl).id`
  - `destination_url`
- From costs:
  - `advertiser_id`
  - `campaign_id`
  - `channel_id`
  - `date`
  - `spend`
  - `fees.agency_fees`
  - `fees.platform_fees`

4. Analyze arrays and nesting
- `attribution_results.credits` is an array of objects, so it must be expanded with `UNNEST`
- No other array needs to be unnested for the main logic
- Campaign and creative outputs should be accumulated as arrays, so `ARRAY_AGG(DISTINCT ...)` is appropriate
- The spend lookup can use campaign membership against the aggregated campaign array

5. Decide join / nest strategy
- Start from attribution results and `UNNEST` credits because revenue lives at the credit-row level
- Join credit rows to touchpoints using credited `touchpoint_id` against `META(tp).id`
- Join touchpoints to tracking links using `tp.tracking_link_id` against `META(tl).id`
- For spend, an outer-style preservation is needed conceptually because URLs with revenue but no spend must remain. A good way is to compute spend separately and use a scalar correlated subquery in projection, which naturally returns null when no matching spend exists instead of filtering the row out
- CTEs are useful because the logic has clear stages:
  - December credit extraction
  - revenue aggregation by URL
  - ranking per advertiser
  - spend aggregation by campaign/channel

6. Determine filters and aggregations
- December 2025 credit filter:
  - filter credit `occurred_at` from `2025-12-01T00:00:00Z` inclusive to `2026-01-01T00:00:00Z` exclusive
  - using the credit timestamp is correct because the question asks what drove attributed revenue in December
- Revenue aggregation:
  - group by advertiser and destination URL
  - sum `credited_revenue`, defaulting missing/null revenue to 0 with `IFMISSINGORNULL`
  - collect distinct campaign IDs and creative IDs contributing to that URL
- Ranking:
  - use `ROW_NUMBER()` partitioned by advertiser and ordered by attributed revenue descending
  - filter to top 5 per advertiser
- Spend aggregation:
  - group costs by advertiser, campaign, and channel for December 2025 dates
  - sum spend plus agency and platform fees, defaulting missing fees to 0
- Spend matching back to ranked URLs:
  - use correlated subquery over aggregated spend data
  - match advertiser and campaigns associated with the URL
  - sum the spend rows for those related campaigns
- Important nuance:
  - although spend was aggregated by campaign and channel, the final spend lookup can sum over matching campaigns for the advertiser
  - this preserves URLs without spend because absence of matches yields null rather than dropping the revenue row

7. Decide output shape
- Return only fields requested or implied:
  - advertiser_id
  - destination_url
  - attributed_revenue
  - campaign_ids
  - creative_ids
  - spend_with_fees_for_related_campaigns
- Use meaningful aliases for computed values
- Sort final results by advertiser and attributed revenue descending so each advertiser’s top URLs appear in ranked order
- No DISTINCT needed in the outer select because grouping/ranking already define one row per advertiser+URL result

8. Validate SQL++ constraints
- After aliasing, every field reference should use the correct alias (`ar`, `cr`, `tp`, `tl`, etc.)
- `META(tp).id` and `META(tl).id` must be used because aliases are defined and joins are against document IDs
- Grouping rules:
  - revenue-by-URL stage groups by advertiser and destination URL, and all other selected fields there are aggregates
  - spend aggregation groups by advertiser, campaign, channel, and selected non-aggregates match that grouping
- `UNNEST` should reference the parent alias correctly: the credits array belongs to `ar`
- String literals for dates and timestamps should use double quotes
- `date` in costs is a field name that may need identifier escaping when referenced
- Window function ranking should be computed after revenue aggregation, not before

9. Overall construction plan
- First derive December credit-level records from attribution results by unnesting credits and keeping advertiser, touchpoint, campaign, creative, channel, and revenue
- Next join those rows to touchpoints and tracking links to obtain destination URLs, then aggregate revenue and collect campaign/creative IDs per advertiser+URL
- Then rank URLs within each advertiser by attributed revenue and keep the top 5
- Separately aggregate December costs into spend-with-fees by advertiser+campaign+channel
- Finally project the ranked URL rows and attach spend via a correlated sum over the spend CTE, matching advertiser and related campaigns so rows with missing spend are still included
</think>

```sql++
WITH dec_credits AS ( SELECT ar.advertiser_id, cr.touchpoint_id, cr.campaign_id, cr.creative_id, cr.channel_id, IFMISSINGORNULL(cr.credited_revenue,0) AS credited_revenue FROM `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`attribution_results` AS ar UNNEST ar.credits AS cr WHERE cr.occurred_at >= "2025-12-01T00:00:00Z" AND cr.occurred_at < "2026-01-01T00:00:00Z" ), rev_by_url AS ( SELECT dc.advertiser_id, tl.destination_url, SUM(dc.credited_revenue) AS attributed_revenue, ARRAY_AGG(DISTINCT dc.campaign_id) AS campaign_ids, ARRAY_AGG(DISTINCT dc.creative_id) AS creative_ids FROM dec_credits AS dc INNER JOIN `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`touchpoints` AS tp ON META(tp).id = dc.touchpoint_id INNER JOIN `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`tracking_links` AS tl ON META(tl).id = tp.tracking_link_id WHERE tl.destination_url IS NOT NULL GROUP BY dc.advertiser_id, tl.destination_url ), ranked AS ( SELECT r.*, ROW_NUMBER() OVER (PARTITION BY r.advertiser_id ORDER BY r.attributed_revenue DESC) AS rn FROM rev_by_url AS r ), spend_by_campaign_channel AS ( SELECT c.advertiser_id, c.campaign_id, c.channel_id, SUM(c.spend + IFMISSINGORNULL(c.fees.agency_fees,0) + IFMISSINGORNULL(c.fees.platform_fees,0)) AS spend_with_fees FROM `training_bucket`.`Advertising_Attribution_and_ROI_Analytics_scope`.`costs` AS c WHERE c.`date` BETWEEN "2025-12-01" AND "2025-12-31" GROUP BY c.advertiser_id, c.campaign_id, c.channel_id ) SELECT rk.advertiser_id, rk.destination_url, rk.attributed_revenue, rk.campaign_ids, rk.creative_ids, (SELECT RAW SUM(s.spend_with_fees) FROM spend_by_campaign_channel AS s WHERE s.advertiser_id = rk.advertiser_id AND s.campaign_id IN rk.campaign_ids)[0] AS spend_with_fees_for_related_campaigns FROM ranked AS rk WHERE rk.rn <= 5 ORDER BY rk.advertiser_id, rk.attributed_revenue DESC

<|im_end|>