Speed Bencmarks

Cluster Speed Benchmarks

NER (BiLSTM-CNN-Char Architecture) Benchmark Experiment

• Dataset: 1000 Clinical Texts from MTSamples Oncology Dataset, approx. 500 tokens per text.
• Driver : Standard_D4s_v3 - 16 GB Memory - 4 Cores
• Enable Autoscaling : False
• Cluster Mode : Standart
• Worker :
  • Standard_D4s_v3 - 16 GB Memory - 4 Cores
  • Standard_D4s_v2 - 28 GB Memory - 8 Cores
• Versions:
  • Databricks Runtime Version : 8.3(Scala 2.12, Spark 3.1.1)
  • spark-nlp Version: v3.2.3
  • spark-nlp-jsl Version : v3.2.3
  • Spark Version : v3.1.1

• Spark NLP Pipeline:

  nlpPipeline = Pipeline(stages=[
        documentAssembler,
        sentenceDetector,
        tokenizer,  
        embeddings_clinical,  
        clinical_ner,  
        ner_converter
        ])
  
  

NOTES:

• The first experiment with 5 different cluster configurations : ner_chunk as a column in Spark NLP Pipeline (ner_converter) output data frame, exploded (lazy evaluation) as ner_chunk and ner_label. Then results were written as parquet and delta formats.

• A second experiment with 2 different cluster configuration : Spark NLP Pipeline output data frame (except word_embeddings column) was written as parquet and delta formats.

• In the first experiment with the most basic driver node and worker (1 worker x 4 cores) configuration selection, it took 4.64 mins and 4.53 mins to write 4 partitioned data as parquet and delta formats respectively.

• With basic driver node and 8 workers (x8 cores) configuration selection, it took 40 seconds and 22 seconds to write 1000 partitioned data as parquet and delta formats respectively.

• In the second experiment with basic driver node and 4 workers (x 4 cores) configuration selection, it took 1.41 mins as parquet and 1.42 mins as delta format to write 16 partitioned (exploded results) data. Without explode it took 1.08 mins as parquet and 1.12 mins as delta format to write the data frame.

• Since given computation durations are highly dependent on different parameters including driver node and worker node configurations as well as partitions, results show that explode method increases duration %10-30 on chosen configurations.

NER Benchmark Tables

Clinical Bert For Token Classification Benchmark Experiment

• Dataset : 7537 Clinical Texts from PubMed Dataset
• Driver : Standard_DS3_v2 - 14GB Memory - 4 Cores
• Enable Autoscaling : True
• Cluster Mode : Standart
• Worker :
  • Standard_DS3_v2 - 14GB Memory - 4 Cores
• Versions :
  • Databricks Runtime Version : 10.0 (Apache Spark 3.2.0, Scala 2.12)
  • spark-nlp Version: v3.4.0
  • spark-nlp-jsl Version : v3.4.0
  • Spark Version : v3.2.0
• Spark NLP Pipeline :

nlpPipeline = Pipeline(stages=[
        documentAssembler,
        sentenceDetector,
        tokenizer,
        ner_jsl_slim_tokenClassifier,
        ner_converter,
        finisher])

NOTES:

• In this experiment, the bert_token_classifier_ner_jsl_slim model was used to measure the inference time of clinical bert for token classification models in the DataBricks environment.

• In the first experiment, the data read from the parquet file is saved as parquet after processing.

• In the second experiment, the data read from the delta table was written to the delta table after it was processed.

NER speed benchmarks across various Spark NLP and PySpark versions

This experiment compares the ClinicalNER runtime for different versions of PySpark and Spark NLP. In this experiment, all reports went through the pipeline 10 times and repeated execution 5 times, so we ran each report 50 times and averaged it, %timeit -r 5 -n 10 run_model(spark, model).

• Driver: Standard Google Colab environment
• Spark NLP Pipeline:

  nlpPipeline = Pipeline(
      stages=[
          documentAssembler,
          sentenceDetector,
          tokenizer,
          word_embeddings,
          clinical_ner,
          ner_converter
          ])
  

• Dataset: File sizes:
  • report_1: ~5.34kb
  • report_2: ~8.51kb
  • report_3: ~11.05kb
  • report_4: ~15.67kb
  • report_5: ~35.23kb

Results show that the different versions can have some variance in the execution time, but the difference is not too relevant.

ChunkMapper and Sentence Entity Resolver Benchmark Experiment

• Dataset: 100 Clinical Texts from MTSamples, approx. 705 tokens and 11 chunks per text.

• Versions:
  • Databricks Runtime Version: 12.2 LTS(Scala 2.12, Spark 3.3.2)
  • spark-nlp Version: v5.2.0
  • spark-nlp-jsl Version: v5.2.0
  • Spark Version: v3.3.2
• Spark NLP Pipelines:

ChunkMapper Pipeline:

    mapper_pipeline = Pipeline().setStages([
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_converter,
        chunkerMapper])

Sentence Entity Resolver Pipeline:

    resolver_pipeline = Pipeline(
        stages = [
            document_assembler,
            sentenceDetectorDL,
            tokenizer,
            word_embeddings,
            ner_model,
            ner_converter,
            c2doc,
            sbert_embedder,
            rxnorm_resolver
      ])

ChunkMapper and Sentence Entity Resolver Pipeline:

mapper_resolver_pipeline = Pipeline(
    stages = [
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_converter,
        chunkerMapper,
        cfModel,
        chunk2doc,
        sbert_embedder,
        rxnorm_resolver,
        resolverMerger
])

NOTES:

• 3 different pipelines: The first pipeline with ChunkMapper, the second with Sentence Entity Resolver, and the third pipeline with ChunkMapper and Sentence Entity Resolver together.

• 3 different configurations: Driver and worker types were kept as same in all cluster configurations. Number of workers were increased gradually and set as 2, 4, 8 for DataBricks. We choosed 3 different configurations for AWS EC2 machines that have same core with DataBricks.

• NER models were kept as same in all pipelines: Pretrained ner_posology_greedy NER model was used in each pipeline.

Benchmark Tables

These figures might differ based on the size of the mapper and resolver models. The larger the models, the higher the inference times. Depending the success rate of mappers (any chunk coming in caught by the mapper successfully), the combined mapper and resolver timing would be less than resolver-only timing.

If the resolver-only timing is equal or very close to the combined mapper and resolver timing, it means that mapper is not capable of catching/ mapping any chunk. In that case, try playing with various parameters in mapper or retrain/ augment the mapper.

• DataBricks Config: 8 CPU Core, 32GiB RAM (2 worker, Standard_DS3_v2)
• AWS Config: 8 CPU Cores, 14GiB RAM (c6a.2xlarge)

• DataBricks Config: 16 CPU Core,64GiB RAM (4 worker, Standard_DS3_v2)
• AWS Config: 16 CPU Cores, 27GiB RAM (c6a.4xlarge)

• DataBricks Config: 32 CPU Core, 128GiB RAM (8 worker, Standard_DS3_v2)
• AWS Config: 32 CPU Cores, 58GiB RAM (c6a.8xlarge)

ONNX and Base Embeddings in Resolver

• Dataset: 100 Custom Clinical Texts, approx. 595 tokens per text
• Versions:
  • spark-nlp Version: v5.2.2
  • spark-nlp-jsl Version : v5.2.1
  • Spark Version : v3.2.1
• Instance Type:
  • 8 CPU Cores 52GiB RAM (Colab Pro - High RAM)

nlp_pipeline = Pipeline(
    stages = [
        document_assembler,
        sentenceDetectorDL,
        tokenizer,
        word_embeddings,
        clinical_ner,
        ner_converter,
  ])

embedding_pipeline = PipelineModel(
    stages = [
        c2doc,
        sbiobert_embeddings # base or onnx version
  ])

resolver_pipeline = PipelineModel(
    stages = [
        rxnorm_resolver
  ])

Results Table

Deidentification Benchmark Experiment

• Dataset: 1000 Clinical Texts from MTSamples, approx. 503 tokens and 6 chunks per text.

• Versions:
  • spark-nlp Version: v5.2.0
  • spark-nlp-jsl Version : v5.2.0
  • Spark Version: v3.3.2
  • DataBricks Config: 32 CPU Core, 128GiB RAM (8 worker)
  • AWS Config: 32 CPU Cores, 58GiB RAM (c6a.8xlarge)
  • Colab Config: 8 CPU Cores 52GiB RAM (Colab Pro - High RAM)
• Spark NLP Pipelines:

Deidentification Pipeline:

deid_pipeline = Pipeline().setStages([
      document_assembler,
      sentence_detector,
      tokenizer,
      word_embeddings,
      deid_ner,
      ner_converter,
      deid_ner_enriched,
      ner_converter_enriched,
      chunk_merge,
      ssn_parser,
      account_parser,
      dln_parser,
      plate_parser,
      vin_parser,
      license_parser,
      country_parser,
      age_parser,
      date_parser,
      phone_parser1,
      phone_parser2,
      ids_parser,
      zip_parser,
      med_parser,
      email_parser,
      chunk_merge1,
      chunk_merge2,
      deid_masked_rgx,
      deid_masked_char,
      deid_masked_fixed_char,
      deid_obfuscated,
      finisher])

Dataset: 1000 Clinical Texts from MTSamples, approx. 503 tokens and 21 chunks per text.

AWS EMR Cluster Benchmark

• Dataset: 340 Custom Clinical Texts, approx. 235 tokens per text
• Versions:
  • EMR Version: ERM.6.15.0
  • spark-nlp Version: v5.2.2
  • spark-nlp-jsl Version : v5.2.1
  • Spark Version : v3.4.1
• Instance Type:
  • Primary: m4.4xlarge, 16 vCore, 64 GiB memory
  • Worker : m4.4xlarge, 16 vCore, 64 GiB memory

Spark NLP Pipeline:

ner_pipeline = Pipeline(stages = [
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_jsl,
        ner_jsl_converter])

resolver_pipeline = Pipeline(stages = [
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_jsl,
        ner_jsl_converter,
        chunk2doc,
        sbert_embeddings,
        snomed_resolver]) 

NOTES: ner_jsl model is used as ner model.The inference time was calculated. The timer started with model.transform(df) and ended with writing results in parquet format.

The sbiobertresolve_snomed_findings model is used as the resolver model. The inference time was calculated. The timer started with model.transform(df) and ended with writing results (snomed_code and snomed_code_definition) in parquet format and 722 entities saved.

Results Table

Deidentification Pipelines Cost Benchmarks [March-2024]

• Versions:
  • spark-nlp Version: v5.2.2
  • spark-nlp-jsl Version : v5.2.1
  • Spark Version : v3.4.1
• EMR
  • Dataset: 10K Custom Clinical Texts, approx. 500 tokens & 15 chunks per text
  • EMR Version: ERM.6.15.0
  • Instance Type:
    • Primary: c5.4xlarge, 16 vCore, 32 GiB memory
    • Worker: m5.16xlarge, 64 vCore, 256 GiB memory, 4 workers
  • Price 12.97 $/hr
• EC2 instance
  • Dataset: 10K Custom Clinical Texts, approx. 500 tokens & 15 chunks per text
  • Instance Type: c5.18xlarge, 72 vCore, 144 GiB memory, Single Instance
  • Price 3.06 $/hr
• Databricks
  • Dataset: 1K Clinical Texts from MT Samples, approx. 503 tokens & 21 chunks per text
  • Instance Type: 32 CPU Core, 128GiB RAM , 8 workers
  • Price 2.7 $/hr

Utilized Pretrained DEID Pipelines:

Optimized Pipeline:

from sparknlp.pretrained import PretrainedPipeline

pipeline_optimized = PretrainedPipeline("clinical_deidentification_subentity_optimized", "en", "clinical/models")

pipeline_optimized = Pipeline().setStages(
    [document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    deid_ner,
    ner_converter,
    ssn_parser,
    account_parser
    dln_parser,
    plate_parser,
    vin_parser,
    license_parser,
    country_extracter,
    age_parser,
    date_matcher,
    phone_parser,
    zip_parser,
    med_parser,
    email_parser,
    merger_parser,
    merger_chunks,
    deid_ner_obfus,
    finisher]

Base Pipeline:

from sparknlp.pretrained import PretrainedPipeline

pipeline_base = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")

pipeline_base = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    deid_ner,
    ner_converter,
    deid_ner_enriched,
    ner_converter_enriched,
    chunk_merge,
    ssn_parser,
    account_parser,
    dln_parser,
    plate_parser,
    vin_parser,
    license_parser,
    country_parser,
    age_parser,
    date_parser,
    phone_parser1,
    phone_parser2,
    ids_parser,
    zip_parser,
    med_parser,
    email_parser,
    chunk_merge1,
    chunk_merge2,
    deid_masked_rgx,
    deid_masked_char,
    deid_masked_fixed_char,
    deid_obfuscated,
    finisher])

Estimated Minimum Costs:

• EMR Base Pipeline: partition number: 256, 10K cost:$1.04, 1M cost:$104.41
• EMR Optimized Pipeline: partition number: 256, 10K cost:$0.54, 1M cost:$54.04
• EC2 Instance Base Pipeline: partition number: 512, 10K cost:$0.36, 1M cost:$35.70
• EC2 Instance Optimized Pipeline: partition number: 1024, 10K cost:$0.18, 1M cost:$17.85
• DataBricks Base Pipeline: partition number: 1024, 10K cost:$0.46, 1M cost:$45.76
• DataBricks Optimized Pipeline: partition number: 1024, 10K cost:$0.27, 1M cost:$27.13

CPU NER Benchmarks

NER (BiLSTM-CNN-Char Architecture) CPU Benchmark Experiment

• Dataset: 1000 Clinical Texts from MTSamples Oncology Dataset, approx. 500 tokens per text.
• Versions:
  • spark-nlp Version: v3.4.4
  • spark-nlp-jsl Version: v3.5.2
  • Spark Version: v3.1.2
• Spark NLP Pipeline:

  nlpPipeline = Pipeline(stages=[
        documentAssembler,
        sentenceDetector,
        tokenizer,  
        embeddings_clinical,  
        clinical_ner,  
        ner_converter
        ])

NOTE:

• Spark NLP Pipeline output data frame (except word_embeddings column) was written as parquet format in transform benchmarks.

GPU vs CPU benchmark

This section includes a benchmark for MedicalNerApproach(), comparing its performance when running in m5.8xlarge CPU vs a Tesla V100 SXM2 GPU, as described in the Machine Specs section below.

Big improvements have been carried out from version 3.3.4, so please, make sure you use at least that version to fully levearge Spark NLP capabilities on GPU.

Machine specs

CPU

An AWS m5.8xlarge machine was used for the CPU benchmarking. This machine consists of 32 vCPUs and 128 GB of RAM, as you can check in the official specification webpage available here

GPU

A Tesla V100 SXM2 GPU with 32GB of memory was used to calculate the GPU benchmarking.

Versions

The benchmarking was carried out with the following Spark NLP versions:

Spark version: 3.0.2

Hadoop version: 3.2.0

SparkNLP version: 3.3.4

SparkNLP for Healthcare version: 3.3.4

Spark nodes: 1

Benchmark on MedicalNerDLApproach()

This experiment consisted of training a Name Entity Recognition model (token-level), using our class NerDLApproach(), using Bert Word Embeddings and a Char-CNN-BiLSTM Neural Network. Only 1 Spark node was used for the training.

We used the Spark NLP class MedicalNer and it’s method Approach() as described in the documentation.

The pipeline looks as follows:

Dataset

The size of the dataset was small (17K), consisting of:

Training (rows): 14041

Test (rows): 3250

Training params

Different batch sizes were tested to demonstrate how GPU performance improves with bigger batches compared to CPU, for a constant number of epochs and learning rate.

Epochs: 10

Learning rate: 0.003

Batch sizes: 32, 64, 256, 512, 1024, 2048

Results

Even for this small dataset, we can observe that GPU is able to beat the CPU machine by a 62% in training time and a 68% in inference times. It’s important to mention that the batch size is very relevant when using GPU, since CPU scales much worse with bigger batch sizes than GPU.

Training times depending on batch (in minutes)

Inference times (in minutes)

Although CPU times in inference remain more or less constant regardless the batch sizes, GPU time experiment good improvements the bigger the batch size is.

CPU times: ~29 min

Performance metrics

A macro F1-score of about 0.92 (0.90 in micro) was achieved, with the following charts extracted from the MedicalNerApproach() logs:

Takeaways: How to get the best of the GPU

You will experiment big GPU improvements in the following cases:

1. Embeddings and Transformers are used in your pipeline. Take into consideration that GPU will performance very well in Embeddings / Transformer components, but other components of your pipeline may not leverage as well GPU capabilities;
2. Bigger batch sizes get the best of GPU, while CPU does not scale with bigger batch sizes;
3. Bigger dataset sizes get the best of GPU, while may be a bottleneck while running in CPU and lead to performance drops;

MultiGPU Inference on Databricks

In this part, we will give you an idea on how to choose appropriate hardware specifications for Databricks. Here is a few different hardwares, their prices, as well as their performance:

Apparently, GPU hardware is the cheapest among them although it performs the best. Let’s see how overall performance looks like:

Figure above clearly shows us that GPU should be the first option of ours.

In conclusion, please find the best specifications for your use case since these benchmarks might depend on dataset size, inference batch size, quickness, pricing and so on.

Please refer to this video for further info: https://events.johnsnowlabs.com/webinar-speed-optimization-benchmarks-in-spark-nlp-3-making-the-most-of-modern-hardware?hsCtaTracking=a9bb6358-92bd-4cf3-b97c-e76cb1dfb6ef%7C4edba435-1adb-49fc-83fd-891a7506a417

MultiGPU training

Currently, we don’t support multiGPU training, meaning training 1 model in different GPUs in parallel. However, you can train different models in different GPUs.

MultiGPU inference

Spark NLP can carry out MultiGPU inference if GPUs are in different cluster nodes. For example, if you have a cluster with different GPUs, you can repartition your data to match the number of GPU nodes and then coalesce to retrieve the results back to the master node.

Currently, inference on multiple GPUs on the same machine is not supported.

Where to look for more information about Training

Please, take a look at the Spark NLP and Spark NLP for Healthcare Training sections, and feel free to reach us out in case you want to maximize the performance on your GPU.

Spark NLP vs Spacy Pandas UDF with Arrow Benchmark

This benchmarking report aims to provide a comprehensive comparison between two NLP frameworks on Spark clusters: Spark NLP and SpaCy, specifically in the context of Pandas UDF with Arrow optimization.

Spark NLP is a distributed NLP library built on top of Apache Spark, designed to handle large-scale NLP tasks efficiently. On the other hand, SpaCy is a popular NLP library in single-machine environments.

In this benchmark, we evaluate the performance of both frameworks using Pandas UDF with Arrow, a feature that enhances data transfer between Apache Arrow and Pandas DataFrames, potentially leading to significant performance gains. We will use Spacy as a UDF in Spark to compare the performance of both frameworks.

The benchmark covers a range of common NLP tasks, including Named Entity Recognition (NER) and getting Roberta sentence embeddings.

We calculated the time for both arrow enabled and disabled pandas udf for each task. We reset the notebook before each task to ensure that the results are not affected by the previous task.

Machine specs

Azure Databricks Standard_DS3_v2 machine (6 workers + 1 driver) was used for the CPU benchmarking. This machine consists of 4 CPUs and 14 GB of RAM.

Versions

The benchmarking was carried out with the following versions:

Spark version: 3.1.2

SparkNLP version: 5.1.0

spaCy version: 3.6.1

Spark nodes: 7 (1 driver, 6 workers)

Dataset

The size of the dataset is (120K), consisting of news articles that can be found here.

Benchmark on Named Entity Recognition (NER)

Named Entity Recognition (NER) is the process of identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, etc. In this benchmark, we compare the performance of Spark NLP and SpaCy in recognizing named entities in a text column.

The following pipeline shows how to recognize named entities in a text column using Spark NLP:

glove_embeddings = WordEmbeddingsModel.pretrained('glove_100d').\
    setInputCols(["document", 'token']).\
    setOutputCol("embeddings")

public_ner = NerDLModel.pretrained("ner_dl", 'en') \
    .setInputCols(["document", "token", "embeddings"]) \
    .setOutputCol("ner")

pipeline = Pipeline(stages=[document_assembler,
                              tokenizer,
                              glove_embeddings,
                              public_ner
                           ])

SpaCy uses the following pandas UDF to recognize named entities in a text column. We exclude the tagger, parser, attribute ruler, and lemmatizer components to make the comparison fair.

nlp_ner = spacy.load("en_core_web_sm", exclude=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"])

# Define a UDF to  perform NER
@pandas_udf(ArrayType(StringType()))
def ner_with_spacy(text_series):
    entities_list = []
    for text in text_series:
        doc = nlp_ner(text)
        entities = [f"{ent.text}:::{ent.label_}" for ent in doc.ents]
        entities_list.append(entities)
    return pd.Series(entities_list)

Benchmark on Getting Roberta Sentence Embeddings

In this benchmark, we compare the performance of Spark NLP and SpaCy in getting Roberta sentence embeddings for a text column.

The following pipeline shows how to get Roberta embeddings for a text column using Spark NLP:

embeddings = RoBertaSentenceEmbeddings.pretrained("sent_roberta_base", "en") \
      .setInputCols("document") \
      .setOutputCol("embeddings")

pipeline= Pipeline(stages=[document_assembler,
                            embeddings
                           ])

SpaCy uses the following pandas UDF.

nlp_embeddings = spacy.load("en_core_web_trf")

# Define a UDF to get sentence embeddings 
@pandas_udf(ArrayType(FloatType()))
def embeddings_with_spacy(text_series):
    embeddings_list = []
    for text in text_series:
        doc = nlp_embeddings(text)
        embeddings = doc._.trf_data.tensors[-1][0]
        embeddings_list.append(embeddings)
    return pd.Series(embeddings_list)

Results

Both frameworks were tested on a dataset of 120K rows. SpaCy was tested with and without Arrow enabled. Both frameworks utilized distributed computing to process the data in parallel.

The following table shows the time taken by each framework to perform the tasks mentioned above:

Conclusions

In our analysis, we delved into the performance of two Natural Language Processing (NLP) libraries: Spark NLP and SpaCy. While Spark NLP, seamlessly integrated with Apache Spark, excels in managing extensive NLP tasks on distributed systems and large datasets, SpaCy is used particularly in single-machine environments.

The results of our evaluation highlight clear disparities in processing times across the assessed tasks. In NER extraction, Spark NLP demonstrated exceptional efficiency, completing the task in a mere 3 minutes and 35 seconds. In contrast, Spacy UDF with Arrow and Spacy UDF without Arrow took 4 minutes and 49 seconds, and 5 minutes and 4 seconds, respectively. Moving on to the generation of Roberta embeddings, Spark NLP once again proved its prowess, completing the task in 22 minutes and 16 seconds. Meanwhile, Spacy UDF with Arrow and Spacy UDF without Arrow required 29 minutes and 27 seconds, and 29 minutes and 30 seconds, respectively.

These findings unequivocally affirm Spark NLP’s superiority for NER extraction tasks, and its significant time advantage for tasks involving Roberta embeddings.

Additional Comments

• Scalability:

Spark NLP: Built on top of Apache Spark, Spark NLP is inherently scalable and distributed. It is designed to handle large-scale data processing with distributed computing resources. It is well-suited for processing vast amounts of data across multiple nodes.

SpaCy with pandas UDFs: Using SpaCy within a pandas UDF (User-Defined Function) and Arrow for efficient data transfer can bring SpaCy’s abilities into the Spark ecosystem. However, while Arrow optimizes the serialization and deserialization between JVM and Python processes, the scalability of this approach is still limited by the fact that the actual NLP processing is single-node (by SpaCy) for each partition of your Spark DataFrame.

• Performance:

Spark NLP: Since it’s natively built on top of Spark, it is optimized for distributed processing. The performance is competitive, especially when you are dealing with vast amounts of data that need distributed processing.

SpaCy with pandas UDFs: SpaCy is fast for single-node processing. The combination of SpaCy with Arrow-optimized UDFs can be performant for moderate datasets or tasks. However, you might run into bottlenecks when scaling to very large datasets unless you have a massive Spark cluster.

• Ecosystem Integration:

Spark NLP: Being a Spark-native library, Spark NLP integrates seamlessly with other Spark components, making it easier to build end-to-end data processing pipelines.

SpaCy with pandas UDFs: While the integration with Spark is possible, it’s a bit more ‘forced.’ It requires careful handling, especially if you’re trying to ensure optimal performance.

• Features & Capabilities:

Spark NLP: Offers a wide array of NLP functionalities, including some that are tailored for the healthcare domain. It’s continuously evolving and has a growing ecosystem.

SpaCy: A popular library for NLP with extensive features, optimizations, and pre-trained models. However, certain domain-specific features in Spark NLP might not have direct counterparts in SpaCy.

• Development & Maintenance:

Spark NLP: As with any distributed system, development and debugging might be more complex. You have to consider factors inherent to distributed systems.

SpaCy with pandas UDFs: Development might be more straightforward since you’re essentially working with Python functions. However, maintaining optimal performance with larger datasets and ensuring scalability can be tricky.