Re: [PySpark] Tagging descriptions
Thanks everyone. While working on Tagging I stumbled upon another setback.. There are about 5000 regex I am dealing with, out of with couple of hundreds have variable length lookbehind (originally these worked in a JVM). In order to use this with Python/Pyspark udf - we need to either modify these regex rules so that it can work with Python or move this to scala/java based implementation.. Does anyone have any experience with variable length lookbehind (quantifiers/alternations of variable length) in python/pyspark? Any suggestions are much appreciated! Thanks, -Rishi On Thu, May 14, 2020 at 2:57 PM Netanel Malka wrote: > For elasticsearch you can use the elastic official connector. > https://www.elastic.co/what-is/elasticsearch-hadoop > > Elastic spark connector docs: > https://www.elastic.co/guide/en/elasticsearch/hadoop/current/spark.html > > > > On Thu, May 14, 2020, 21:14 Amol Umbarkar wrote: > >> Check out sparkNLP for tokenization. I am not sure about solar or elastic >> search though >> >> On Thu, May 14, 2020 at 9:02 PM Rishi Shah >> wrote: >> >>> This is great, thanks you Zhang & Amol !! >>> >>> Yes we can have multiple tags per row and multiple regex applied to >>> single row as well. Would you have any example of working with spark & >>> search engines like Solar, ElasticSearch? Does Spark ML provide >>> tokenization support as expected (I am yet to try SparkML, still a >>> beginner)? >>> >>> Any other reference material you found useful while working on similar >>> problem? appreciate all the help! >>> >>> Thanks, >>> -Rishi >>> >>> >>> On Thu, May 14, 2020 at 6:11 AM Amol Umbarkar >>> wrote: >>> Rishi, Just adding to zhang's questions. Are you expecting multiple tags per row? Do you check multiple regex for a single tag? Let's say you had only one tag then theoretically you should be do this - 1 Remove stop words or any irrelevant stuff 2 split text into equal sized chunk column (eg - if max length is 1000chars, split into 20 columns of 50 chars) 3 distribute work for each column that would result in binary (true/false) for a single tag 4 merge the 20 resulting columns 5 repeat for other tags or do them in parallel 3 and 4 for them Note on 3: If you expect single tag per row, then you can repeat 3 column by column and skip rows that have got tags in prior step. Secondly, if you expect similarity in text (of some kind) then you could jus work on unique text values (might require shuffle, hence expensive) and then join the end result back to the original data. You could use hash of some kind to join back. Though I would go for this approach only if the chances of similarity in text are very high (it could be in your case for being transactional data). Not the full answer to your question but hope this helps you brainstorm more. Thanks, Amol On Wed, May 13, 2020 at 10:17 AM Rishi Shah wrote: > Thanks ZHANG! Please find details below: > > # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a > parquet formatted data so, need to worry about only the columns to be > tagged) > > avg length of the text to be parsed : ~300 > > Unfortunately don't have sample data or regex which I can share > freely. However about data being parsed - assume these are purchases made > online and we are trying to parse the transaction details. Like purchases > made on amazon can be tagged to amazon as well as other vendors etc. > > Appreciate your response! > > > > On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: > >> May I get some requirement details? >> >> Such as: >> 1. The row count and one row data size >> 2. The avg length of text to be parsed by RegEx >> 3. The sample format of text to be parsed >> 4. The sample of current RegEx >> >> -- >> Cheers, >> -z >> >> On Mon, 11 May 2020 18:40:49 -0400 >> Rishi Shah wrote: >> >> > Hi All, >> > >> > I have a tagging problem at hand where we currently use regular >> expressions >> > to tag records. Is there a recommended way to distribute & tag? >> Data is >> > about 10TB large. >> > >> > -- >> > Regards, >> > >> > Rishi Shah >> > > > -- > Regards, > > Rishi Shah > >>> >>> -- >>> Regards, >>> >>> Rishi Shah >>> >> -- Regards, Rishi Shah
Re: [PySpark] Tagging descriptions
For elasticsearch you can use the elastic official connector. https://www.elastic.co/what-is/elasticsearch-hadoop Elastic spark connector docs: https://www.elastic.co/guide/en/elasticsearch/hadoop/current/spark.html On Thu, May 14, 2020, 21:14 Amol Umbarkar wrote: > Check out sparkNLP for tokenization. I am not sure about solar or elastic > search though > > On Thu, May 14, 2020 at 9:02 PM Rishi Shah > wrote: > >> This is great, thanks you Zhang & Amol !! >> >> Yes we can have multiple tags per row and multiple regex applied to >> single row as well. Would you have any example of working with spark & >> search engines like Solar, ElasticSearch? Does Spark ML provide >> tokenization support as expected (I am yet to try SparkML, still a >> beginner)? >> >> Any other reference material you found useful while working on similar >> problem? appreciate all the help! >> >> Thanks, >> -Rishi >> >> >> On Thu, May 14, 2020 at 6:11 AM Amol Umbarkar >> wrote: >> >>> Rishi, >>> Just adding to zhang's questions. >>> >>> Are you expecting multiple tags per row? >>> Do you check multiple regex for a single tag? >>> >>> Let's say you had only one tag then theoretically you should be do this - >>> >>> 1 Remove stop words or any irrelevant stuff >>> 2 split text into equal sized chunk column (eg - if max length is >>> 1000chars, split into 20 columns of 50 chars) >>> 3 distribute work for each column that would result in binary >>> (true/false) for a single tag >>> 4 merge the 20 resulting columns >>> 5 repeat for other tags or do them in parallel 3 and 4 for them >>> >>> Note on 3: If you expect single tag per row, then you can repeat 3 >>> column by column and skip rows that have got tags in prior step. >>> >>> Secondly, if you expect similarity in text (of some kind) then you could >>> jus work on unique text values (might require shuffle, hence expensive) and >>> then join the end result back to the original data. You could use hash of >>> some kind to join back. Though I would go for this approach only if the >>> chances of similarity in text are very high (it could be in your case for >>> being transactional data). >>> >>> Not the full answer to your question but hope this helps you brainstorm >>> more. >>> >>> Thanks, >>> Amol >>> >>> >>> >>> >>> >>> On Wed, May 13, 2020 at 10:17 AM Rishi Shah >>> wrote: >>> Thanks ZHANG! Please find details below: # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a parquet formatted data so, need to worry about only the columns to be tagged) avg length of the text to be parsed : ~300 Unfortunately don't have sample data or regex which I can share freely. However about data being parsed - assume these are purchases made online and we are trying to parse the transaction details. Like purchases made on amazon can be tagged to amazon as well as other vendors etc. Appreciate your response! On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: > May I get some requirement details? > > Such as: > 1. The row count and one row data size > 2. The avg length of text to be parsed by RegEx > 3. The sample format of text to be parsed > 4. The sample of current RegEx > > -- > Cheers, > -z > > On Mon, 11 May 2020 18:40:49 -0400 > Rishi Shah wrote: > > > Hi All, > > > > I have a tagging problem at hand where we currently use regular > expressions > > to tag records. Is there a recommended way to distribute & tag? Data > is > > about 10TB large. > > > > -- > > Regards, > > > > Rishi Shah > -- Regards, Rishi Shah >>> >> >> -- >> Regards, >> >> Rishi Shah >> >
Re: [PySpark] Tagging descriptions
Check out sparkNLP for tokenization. I am not sure about solar or elastic search though On Thu, May 14, 2020 at 9:02 PM Rishi Shah wrote: > This is great, thanks you Zhang & Amol !! > > Yes we can have multiple tags per row and multiple regex applied to single > row as well. Would you have any example of working with spark & search > engines like Solar, ElasticSearch? Does Spark ML provide tokenization > support as expected (I am yet to try SparkML, still a beginner)? > > Any other reference material you found useful while working on similar > problem? appreciate all the help! > > Thanks, > -Rishi > > > On Thu, May 14, 2020 at 6:11 AM Amol Umbarkar > wrote: > >> Rishi, >> Just adding to zhang's questions. >> >> Are you expecting multiple tags per row? >> Do you check multiple regex for a single tag? >> >> Let's say you had only one tag then theoretically you should be do this - >> >> 1 Remove stop words or any irrelevant stuff >> 2 split text into equal sized chunk column (eg - if max length is >> 1000chars, split into 20 columns of 50 chars) >> 3 distribute work for each column that would result in binary >> (true/false) for a single tag >> 4 merge the 20 resulting columns >> 5 repeat for other tags or do them in parallel 3 and 4 for them >> >> Note on 3: If you expect single tag per row, then you can repeat 3 column >> by column and skip rows that have got tags in prior step. >> >> Secondly, if you expect similarity in text (of some kind) then you could >> jus work on unique text values (might require shuffle, hence expensive) and >> then join the end result back to the original data. You could use hash of >> some kind to join back. Though I would go for this approach only if the >> chances of similarity in text are very high (it could be in your case for >> being transactional data). >> >> Not the full answer to your question but hope this helps you brainstorm >> more. >> >> Thanks, >> Amol >> >> >> >> >> >> On Wed, May 13, 2020 at 10:17 AM Rishi Shah >> wrote: >> >>> Thanks ZHANG! Please find details below: >>> >>> # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a >>> parquet formatted data so, need to worry about only the columns to be >>> tagged) >>> >>> avg length of the text to be parsed : ~300 >>> >>> Unfortunately don't have sample data or regex which I can share freely. >>> However about data being parsed - assume these are purchases made online >>> and we are trying to parse the transaction details. Like purchases made on >>> amazon can be tagged to amazon as well as other vendors etc. >>> >>> Appreciate your response! >>> >>> >>> >>> On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: >>> May I get some requirement details? Such as: 1. The row count and one row data size 2. The avg length of text to be parsed by RegEx 3. The sample format of text to be parsed 4. The sample of current RegEx -- Cheers, -z On Mon, 11 May 2020 18:40:49 -0400 Rishi Shah wrote: > Hi All, > > I have a tagging problem at hand where we currently use regular expressions > to tag records. Is there a recommended way to distribute & tag? Data is > about 10TB large. > > -- > Regards, > > Rishi Shah >>> >>> >>> -- >>> Regards, >>> >>> Rishi Shah >>> >> > > -- > Regards, > > Rishi Shah >
Re: [PySpark] Tagging descriptions
This is great, thanks you Zhang & Amol !! Yes we can have multiple tags per row and multiple regex applied to single row as well. Would you have any example of working with spark & search engines like Solar, ElasticSearch? Does Spark ML provide tokenization support as expected (I am yet to try SparkML, still a beginner)? Any other reference material you found useful while working on similar problem? appreciate all the help! Thanks, -Rishi On Thu, May 14, 2020 at 6:11 AM Amol Umbarkar wrote: > Rishi, > Just adding to zhang's questions. > > Are you expecting multiple tags per row? > Do you check multiple regex for a single tag? > > Let's say you had only one tag then theoretically you should be do this - > > 1 Remove stop words or any irrelevant stuff > 2 split text into equal sized chunk column (eg - if max length is > 1000chars, split into 20 columns of 50 chars) > 3 distribute work for each column that would result in binary (true/false) > for a single tag > 4 merge the 20 resulting columns > 5 repeat for other tags or do them in parallel 3 and 4 for them > > Note on 3: If you expect single tag per row, then you can repeat 3 column > by column and skip rows that have got tags in prior step. > > Secondly, if you expect similarity in text (of some kind) then you could > jus work on unique text values (might require shuffle, hence expensive) and > then join the end result back to the original data. You could use hash of > some kind to join back. Though I would go for this approach only if the > chances of similarity in text are very high (it could be in your case for > being transactional data). > > Not the full answer to your question but hope this helps you brainstorm > more. > > Thanks, > Amol > > > > > > On Wed, May 13, 2020 at 10:17 AM Rishi Shah > wrote: > >> Thanks ZHANG! Please find details below: >> >> # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a >> parquet formatted data so, need to worry about only the columns to be >> tagged) >> >> avg length of the text to be parsed : ~300 >> >> Unfortunately don't have sample data or regex which I can share freely. >> However about data being parsed - assume these are purchases made online >> and we are trying to parse the transaction details. Like purchases made on >> amazon can be tagged to amazon as well as other vendors etc. >> >> Appreciate your response! >> >> >> >> On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: >> >>> May I get some requirement details? >>> >>> Such as: >>> 1. The row count and one row data size >>> 2. The avg length of text to be parsed by RegEx >>> 3. The sample format of text to be parsed >>> 4. The sample of current RegEx >>> >>> -- >>> Cheers, >>> -z >>> >>> On Mon, 11 May 2020 18:40:49 -0400 >>> Rishi Shah wrote: >>> >>> > Hi All, >>> > >>> > I have a tagging problem at hand where we currently use regular >>> expressions >>> > to tag records. Is there a recommended way to distribute & tag? Data is >>> > about 10TB large. >>> > >>> > -- >>> > Regards, >>> > >>> > Rishi Shah >>> >> >> >> -- >> Regards, >> >> Rishi Shah >> > -- Regards, Rishi Shah
Re: [PySpark] Tagging descriptions
Rishi, Just adding to zhang's questions. Are you expecting multiple tags per row? Do you check multiple regex for a single tag? Let's say you had only one tag then theoretically you should be do this - 1 Remove stop words or any irrelevant stuff 2 split text into equal sized chunk column (eg - if max length is 1000chars, split into 20 columns of 50 chars) 3 distribute work for each column that would result in binary (true/false) for a single tag 4 merge the 20 resulting columns 5 repeat for other tags or do them in parallel 3 and 4 for them Note on 3: If you expect single tag per row, then you can repeat 3 column by column and skip rows that have got tags in prior step. Secondly, if you expect similarity in text (of some kind) then you could jus work on unique text values (might require shuffle, hence expensive) and then join the end result back to the original data. You could use hash of some kind to join back. Though I would go for this approach only if the chances of similarity in text are very high (it could be in your case for being transactional data). Not the full answer to your question but hope this helps you brainstorm more. Thanks, Amol On Wed, May 13, 2020 at 10:17 AM Rishi Shah wrote: > Thanks ZHANG! Please find details below: > > # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a parquet > formatted data so, need to worry about only the columns to be tagged) > > avg length of the text to be parsed : ~300 > > Unfortunately don't have sample data or regex which I can share freely. > However about data being parsed - assume these are purchases made online > and we are trying to parse the transaction details. Like purchases made on > amazon can be tagged to amazon as well as other vendors etc. > > Appreciate your response! > > > > On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: > >> May I get some requirement details? >> >> Such as: >> 1. The row count and one row data size >> 2. The avg length of text to be parsed by RegEx >> 3. The sample format of text to be parsed >> 4. The sample of current RegEx >> >> -- >> Cheers, >> -z >> >> On Mon, 11 May 2020 18:40:49 -0400 >> Rishi Shah wrote: >> >> > Hi All, >> > >> > I have a tagging problem at hand where we currently use regular >> expressions >> > to tag records. Is there a recommended way to distribute & tag? Data is >> > about 10TB large. >> > >> > -- >> > Regards, >> > >> > Rishi Shah >> > > > -- > Regards, > > Rishi Shah >
Re: [PySpark] Tagging descriptions
AFAICT, from the data size (25B rows, key cell 300 chars string), looks like a common Spark job. But the regex might be complex, I guess there are lots of items to match as (apple|banana|cola|...) from the purchase list. Regex matching is a high CPU computing task. If the current performance with more partitions doesn't meet the requirement, the keywords indexing might be a little help -- rather tokenize the purchase list firstly and index them (like a search engine) than RegEx them directly. And there are alos several search engines can work well with Spark, such as Elasticsearch, Solr. -- Cheers, -z On Wed, 13 May 2020 00:47:02 -0400 Rishi Shah wrote: > Thanks ZHANG! Please find details below: > > # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a parquet > formatted data so, need to worry about only the columns to be tagged) > > avg length of the text to be parsed : ~300 > > Unfortunately don't have sample data or regex which I can share freely. > However about data being parsed - assume these are purchases made online > and we are trying to parse the transaction details. Like purchases made on > amazon can be tagged to amazon as well as other vendors etc. > > Appreciate your response! > > > > On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: > > > May I get some requirement details? > > > > Such as: > > 1. The row count and one row data size > > 2. The avg length of text to be parsed by RegEx > > 3. The sample format of text to be parsed > > 4. The sample of current RegEx > > > > -- > > Cheers, > > -z > > > > On Mon, 11 May 2020 18:40:49 -0400 > > Rishi Shah wrote: > > > > > Hi All, > > > > > > I have a tagging problem at hand where we currently use regular > > expressions > > > to tag records. Is there a recommended way to distribute & tag? Data is > > > about 10TB large. > > > > > > -- > > > Regards, > > > > > > Rishi Shah > > > > > -- > Regards, > > Rishi Shah - To unsubscribe e-mail: user-unsubscr...@spark.apache.org
Re: [PySpark] Tagging descriptions
Thanks ZHANG! Please find details below: # of rows: ~25B, row size would be somewhere around ~3-5MB (it's a parquet formatted data so, need to worry about only the columns to be tagged) avg length of the text to be parsed : ~300 Unfortunately don't have sample data or regex which I can share freely. However about data being parsed - assume these are purchases made online and we are trying to parse the transaction details. Like purchases made on amazon can be tagged to amazon as well as other vendors etc. Appreciate your response! On Tue, May 12, 2020 at 6:23 AM ZHANG Wei wrote: > May I get some requirement details? > > Such as: > 1. The row count and one row data size > 2. The avg length of text to be parsed by RegEx > 3. The sample format of text to be parsed > 4. The sample of current RegEx > > -- > Cheers, > -z > > On Mon, 11 May 2020 18:40:49 -0400 > Rishi Shah wrote: > > > Hi All, > > > > I have a tagging problem at hand where we currently use regular > expressions > > to tag records. Is there a recommended way to distribute & tag? Data is > > about 10TB large. > > > > -- > > Regards, > > > > Rishi Shah > -- Regards, Rishi Shah
Re: [PySpark] Tagging descriptions
May I get some requirement details? Such as: 1. The row count and one row data size 2. The avg length of text to be parsed by RegEx 3. The sample format of text to be parsed 4. The sample of current RegEx -- Cheers, -z On Mon, 11 May 2020 18:40:49 -0400 Rishi Shah wrote: > Hi All, > > I have a tagging problem at hand where we currently use regular expressions > to tag records. Is there a recommended way to distribute & tag? Data is > about 10TB large. > > -- > Regards, > > Rishi Shah - To unsubscribe e-mail: user-unsubscr...@spark.apache.org
[PySpark] Tagging descriptions
Hi All, I have a tagging problem at hand where we currently use regular expressions to tag records. Is there a recommended way to distribute & tag? Data is about 10TB large. -- Regards, Rishi Shah
